Artificial Intelligence - intersection with compliance. How AI principles work with compliance principles around data protection. AI and Compliance. AI - SYSC 13.7 - FCA Compliance. AI and regulation. AI and FCA regulation. AI and ICO regulation.
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
The document discusses the ethics of artificial intelligence and outlines both benefits and risks. It begins by introducing speakers on the topic and defining artificial intelligence. It then notes that AI is already used widely to make decisions that affect people's lives. Both benefits of AI like increased precision and risks like job loss requiring retraining are discussed. Concerns are raised by experts like Bill Gates, Elon Musk, and Stephen Hawking about potential existential threats from advanced AI. The document calls for safe and robust AI to avoid negative outcomes through exploration and oversight. It concludes that forward-thinking people are working to address the challenges of ensuring AI is developed and applied responsibly.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
Introduction to the ethics of machine learningDaniel Wilson
A brief introduction to the domain that is variously described as the ethics of machine learning, data science ethics, AI ethics and the ethics of big data. (Delivered as a guest lecture for COMPSCI 361 at the University of Auckland on May 29, 2019)
Artificial intelligence (AI) is a branch of computer science that aims to help machines solve complex problems like humans by borrowing characteristics from human intelligence. AI has many applications in business including credit screening, risk assessment, forecasting, portfolio management, customer analytics, and human resources. The future of AI could include intelligent personal robots and autonomous vehicles networked together. While AI may replace some human jobs, it will likely produce more applications and augment human capabilities rather than replace humans altogether.
What regulation for Artificial Intelligence?Nozha Boujemaa
Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices
The Future of Humanity
Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.
This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.
Infused with technology, we’re asking: what does it means to be human?
Our report examines:
• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another
Methodology
For this report, sparks & honey conducted US-focused research on the future of AI. Together with Heartbeat AI Technologies, we examined the emotional sentiment (feeling and emotions) around artificial intelligence in a Heartbeat AI Pulse Survey of 150 people in the US. Tapping into our Influencer Advisory Board and proprietary cultural intelligence system, we combed through thousands of signals to build a vision of the future of AI. We also interviewed leading experts in the field of artificial intelligence.
Ethical Considerations in the Design of Artificial IntelligenceJohn C. Havens
A presentation for IEEE's Ethics Symposium happening in Vancouver, May 2016. Featuring presentations from John C. Havens, Mike Van der Loos, John P. Sullins, and Alan Mackworth.
This document discusses the ethical issues surrounding artificial intelligence. It begins by noting humanity's long-standing fascination with creating tools that can replace human labor. However, others have warned of the potential harms of AI if not developed with wisdom. The document then outlines some of the common fears associated with AI, such as technology becoming autonomous and reversing the master-servant role between humanity and our creations. It also examines themes from Frankenstein that continue to emerge in science fiction, such as the ambiguity of technology and whether it will ultimately benefit or hinder humanity. The document considers various impacts that highly advanced AI could have, such as economic and educational impacts, and concludes by emphasizing the importance of considering whether just because we can
The document discusses the ethics of artificial intelligence and outlines both benefits and risks. It begins by introducing speakers on the topic and defining artificial intelligence. It then notes that AI is already used widely to make decisions that affect people's lives. Both benefits of AI like increased precision and risks like job loss requiring retraining are discussed. Concerns are raised by experts like Bill Gates, Elon Musk, and Stephen Hawking about potential existential threats from advanced AI. The document calls for safe and robust AI to avoid negative outcomes through exploration and oversight. It concludes that forward-thinking people are working to address the challenges of ensuring AI is developed and applied responsibly.
Learn to identify use cases for machine learning (ML), acquire best practices to frame problems in a way that key stakeholders and senior management can understand and support, and help create the right conditions for delivering successful ML-based solutions to your business.
Introduction to the ethics of machine learningDaniel Wilson
A brief introduction to the domain that is variously described as the ethics of machine learning, data science ethics, AI ethics and the ethics of big data. (Delivered as a guest lecture for COMPSCI 361 at the University of Auckland on May 29, 2019)
Artificial intelligence (AI) is a branch of computer science that aims to help machines solve complex problems like humans by borrowing characteristics from human intelligence. AI has many applications in business including credit screening, risk assessment, forecasting, portfolio management, customer analytics, and human resources. The future of AI could include intelligent personal robots and autonomous vehicles networked together. While AI may replace some human jobs, it will likely produce more applications and augment human capabilities rather than replace humans altogether.
What regulation for Artificial Intelligence?Nozha Boujemaa
Should we regulate Artificial Intelligence? What are the challenges to face bias in data and algorithms? What is trustworthy AI? AI HLEG (European Commission) and AIGO (OECD) feedback experiences and recommendations. Example in precision medicine: AI/ML for medical devices
The Future of Humanity
Through our interaction with machines, we develop emotional, human expectations of them. Alexa, for example, comes alive when we speak with it. AI is and will be a representation of its cultural context, the values and ethics we apply to one another as humans.
This machinery is eerily familiar as it mirrors us, and eventually becomes even smarter than us mere mortals. We’re programming its advantages based on how we see ourselves and the world around us, and we’re doing this at an incredible pace. This shift is pervading culture from our perceptions of beauty and aesthetics to how we interact with one another – and our AI.
Infused with technology, we’re asking: what does it means to be human?
Our report examines:
• The evolution of our empathy from humans to animals and robots
• How we treat AI in its infancy like we do a child, allowing it space to grow
• The spectrum of our emotional comfort in a world embracing AI
• The cultural contexts fueling AI biases, such as gender stereotypes, that drive the direction of AI
• How we place an innate trust in machines, more than we do one another
Methodology
For this report, sparks & honey conducted US-focused research on the future of AI. Together with Heartbeat AI Technologies, we examined the emotional sentiment (feeling and emotions) around artificial intelligence in a Heartbeat AI Pulse Survey of 150 people in the US. Tapping into our Influencer Advisory Board and proprietary cultural intelligence system, we combed through thousands of signals to build a vision of the future of AI. We also interviewed leading experts in the field of artificial intelligence.
Ethical Considerations in the Design of Artificial IntelligenceJohn C. Havens
A presentation for IEEE's Ethics Symposium happening in Vancouver, May 2016. Featuring presentations from John C. Havens, Mike Van der Loos, John P. Sullins, and Alan Mackworth.
Technology for everyone - AI ethics and BiasMarion Mulder
Slides from my talk at #ToonTechTalks on 27 september 2018
We all see the great potential AI is bringing us. But is it really bringing it to everyone? How are we ensuring under-represented groups are included and vulnerable people are protected? What to do when our technology is unintended biased and discriminating against certain groups. And what if the data and AI is correct, but the by-effect of it is that some groups are put at risk? All questions we need to think about when we are advancing technology for the benefit of humanity.
Sharing what I've learned from my work in diversity, digital and from following great minds in this field such as Joanna Bryson, Virginia Dignum, Rumman Chowdhury, Juriaan van Diggelen, Valerie Frissen, Catelijne Muller, and many more.
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
This document discusses some of the major ethical issues related to artificial intelligence. It begins with a disclaimer from the author about their lack of expertise in AI. It then provides brief historical information about the development of concepts leading to the internet. The document defines ethics and artificial intelligence. It proceeds to outline several key ethical issues facing AI, including unemployment and unfair wealth distribution due to automation, human-mimicking AI systems, self-driving car dilemmas, AI bias, concerns about developing lethal autonomous weapons, and debates around abandoning development of advanced AI. It concludes by discussing potential approaches to addressing these issues, such as voluntary regulation and governance of AI as well as opposing campaigns to bans on certain technologies.
The document discusses bringing artificial intelligence (AI) to business intelligence (BI). It provides an overview of the current BI environment and how it is lacking in its ability to answer "why" questions and provide prescriptive recommendations. The document then defines different types of AI, from weak AI to artificial general intelligence. It also outlines various AI technologies, especially machine learning techniques like supervised and unsupervised learning. The overall document serves to introduce the topic of integrating AI capabilities into BI tools and analytics.
The document discusses the need for AI governance frameworks to build trust in AI systems. It outlines key pillars of governance including governing bodies, roles and responsibilities, and standard operating procedures. It also discusses the importance of model documentation, validation and certification. Effective governance requires a risk-based approach and measures to minimize bias and ensure fairness, transparency and human-centric AI. Governance spans the full model lifecycle from data collection and preprocessing to model deployment and use.
9 Examples of Artificial Intelligence in Use TodayIQVIS
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans.
Industry analysts argue that artificial intelligence is the future – but if we look around, we are convinced that it’s not the future – it is the present. The given examples will explain the true meaning and context.
Read as a blog post here. http://www.iqvis.com/blog/9-powerful-examples-of-artificial-intelligence-in-use-today/
The document discusses an AI system called Mithila and its members, providing definitions of artificial intelligence, machine learning, and deep learning. It compares human and artificial intelligence and profiles the founders of AI, John McCarthy and Marvin Minsky. The document then outlines current and future applications of AI including quantum computing, robots, smart cars, and discusses whether future AI will be unstoppable, dangerous, or human-friendly.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
This document discusses the uses of artificial intelligence in fintech. It defines AI as computer systems imitating human thinking, and describes three types: weak AI which completes simple tasks based on programming, strong AI which can learn and adapt to complete tasks more efficiently, and human reasoning AI which can anticipate human responses through machine learning. It explains that fintech industries use AI to enhance customer service by processing large amounts of data quickly to better understand customers, detect fraud, and provide faster, more personalized financial services and guidance. Overall, AI benefits fintech customers by accelerating and improving the convenience, ease of use, and delivery of financial services.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
Artificial intelligence and semantic computing can assist the financial services industry in several ways:
- Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations.
- Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights.
- Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily.
- Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Privacy in AI/ML Systems: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we protect the privacy of users when building large-scale AI based systems? How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of privacy-preserving AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Artificial Intelligence For Digital Transformation PowerPoint Presentation Sl...SlideTeam
Presenting this set of slides with name - Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides. This PPT deck displays eighteen slides with in depth research. Our topic oriented Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
This document provides an overview of artificial intelligence (AI), including its history, types, workings, applications, and pros and cons. It defines AI as the ability of computers to mimic human intelligence through tasks like problem-solving, learning, and decision making. The document outlines the major developments in AI from 1950 to present day. It describes the two main types of AI as narrow/weak AI that can perform specific tasks, versus general and super AI that may match or surpass human-level cognition. The key components and functionalities that enable AI are also summarized, along with its wide-ranging applications and the benefits and limitations of AI systems.
This document discusses artificial intelligence (AI) and its applications in business. It describes AI as the intelligence of machines and the branch of computer science that aims to create machine intelligence through techniques like neural networks, expert systems, and natural language processing. The document outlines how AI is used in various business functions like finance, marketing, human resources, manufacturing, and more to tackle complex problems, analyze data, optimize processes, and increase productivity. It also provides examples of specific AI applications in credit screening, forecasting, customer relationship management, and manufacturing scheduling.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
The document discusses the challenges faced by corporate privacy departments and how they can better align with other business functions. It recommends that privacy departments find synergies with information security, product development, legal and other teams. It provides examples of how privacy can collaborate with different departments on tasks like product analysis, incident response and metrics. The document also outlines good practices for privacy programs, such as using recognized frameworks, conducting privacy assessments and demonstrating value through objective metrics.
GETTINGGDPR-READY MEANS SETTING UP A PRIVACY MANAGEMENT SYSTEM,
BEING ABLE TO SHOW IT AND KEEPING IT EFFECTIVE
A management system is a “living” entity which adapts to business context (new markets-products-services, M&A, demerge, law/policies changes, … ) and improves over time
Technology for everyone - AI ethics and BiasMarion Mulder
Slides from my talk at #ToonTechTalks on 27 september 2018
We all see the great potential AI is bringing us. But is it really bringing it to everyone? How are we ensuring under-represented groups are included and vulnerable people are protected? What to do when our technology is unintended biased and discriminating against certain groups. And what if the data and AI is correct, but the by-effect of it is that some groups are put at risk? All questions we need to think about when we are advancing technology for the benefit of humanity.
Sharing what I've learned from my work in diversity, digital and from following great minds in this field such as Joanna Bryson, Virginia Dignum, Rumman Chowdhury, Juriaan van Diggelen, Valerie Frissen, Catelijne Muller, and many more.
AI Governance and Ethics - Industry StandardsAnsgar Koene
Presentation on the potential for Ethics based Industry Standards to function as vehicle to address socio-technical challenges from AI.
Presentation given at the the 1st Austrian IFIP forum ono "AI and future society".
This document discusses some of the major ethical issues related to artificial intelligence. It begins with a disclaimer from the author about their lack of expertise in AI. It then provides brief historical information about the development of concepts leading to the internet. The document defines ethics and artificial intelligence. It proceeds to outline several key ethical issues facing AI, including unemployment and unfair wealth distribution due to automation, human-mimicking AI systems, self-driving car dilemmas, AI bias, concerns about developing lethal autonomous weapons, and debates around abandoning development of advanced AI. It concludes by discussing potential approaches to addressing these issues, such as voluntary regulation and governance of AI as well as opposing campaigns to bans on certain technologies.
The document discusses bringing artificial intelligence (AI) to business intelligence (BI). It provides an overview of the current BI environment and how it is lacking in its ability to answer "why" questions and provide prescriptive recommendations. The document then defines different types of AI, from weak AI to artificial general intelligence. It also outlines various AI technologies, especially machine learning techniques like supervised and unsupervised learning. The overall document serves to introduce the topic of integrating AI capabilities into BI tools and analytics.
The document discusses the need for AI governance frameworks to build trust in AI systems. It outlines key pillars of governance including governing bodies, roles and responsibilities, and standard operating procedures. It also discusses the importance of model documentation, validation and certification. Effective governance requires a risk-based approach and measures to minimize bias and ensure fairness, transparency and human-centric AI. Governance spans the full model lifecycle from data collection and preprocessing to model deployment and use.
9 Examples of Artificial Intelligence in Use TodayIQVIS
Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans.
Industry analysts argue that artificial intelligence is the future – but if we look around, we are convinced that it’s not the future – it is the present. The given examples will explain the true meaning and context.
Read as a blog post here. http://www.iqvis.com/blog/9-powerful-examples-of-artificial-intelligence-in-use-today/
The document discusses an AI system called Mithila and its members, providing definitions of artificial intelligence, machine learning, and deep learning. It compares human and artificial intelligence and profiles the founders of AI, John McCarthy and Marvin Minsky. The document then outlines current and future applications of AI including quantum computing, robots, smart cars, and discusses whether future AI will be unstoppable, dangerous, or human-friendly.
Artificial intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. There are four main schools of thought in AI: thinking humanly, thinking rationally, acting humanly, and acting rationally. Popular techniques used in AI include machine learning, deep learning, and natural language processing. The document then discusses the growth of AI and its applications in various domains like healthcare, law, education, and more. It also lists the top companies leading the development of AI like DeepMind, Google, Facebook, Microsoft, and others. Finally, it provides perspectives on the future impact and adoption of AI.
Contemporary AI engenders hopes and fears – hopes of harnessing AI for productivity growth and innovation – fears of mass unemployment and conflict between humankind and an artificial super-intelligence. Before we let AI drive our hopes and fears, we need to understand what it is and what it is not. Then we need to understand how to implement AI in an ethical and responsible manner. Only then can we harness the power of AI to our benefit.
This document discusses the uses of artificial intelligence in fintech. It defines AI as computer systems imitating human thinking, and describes three types: weak AI which completes simple tasks based on programming, strong AI which can learn and adapt to complete tasks more efficiently, and human reasoning AI which can anticipate human responses through machine learning. It explains that fintech industries use AI to enhance customer service by processing large amounts of data quickly to better understand customers, detect fraud, and provide faster, more personalized financial services and guidance. Overall, AI benefits fintech customers by accelerating and improving the convenience, ease of use, and delivery of financial services.
This document provides an overview of artificial intelligence (AI) including definitions of different types of AI, a brief history of AI, potential application fields and use cases, and the future outlook for AI. It defines AI as ranging from everyday applications to self-driving cars. It discusses narrow AI, general AI, and superintelligence. The document also summarizes key milestones in the development of AI from 1955 to the present and potential opportunities and challenges of AI including automation, ethics, and politics. It provides examples of Austrian AI startups and their technologies. The outlook suggests that human-level AI may be achieved by 2040 and superintelligence by 2060 with impacts on robotics, climate change, human enhancement, and autonomous
Artificial intelligence and semantic computing can assist the financial services industry in several ways:
- Machine learning and neural networks can analyze large amounts of data to detect patterns and make predictions about customer behavior, risks, and opportunities. This includes predictive analytics, risk analysis, and personalized recommendations.
- Natural language processing allows customers to interact with services using human language across different channels. It also enables analysis of unstructured data like text to gain insights.
- Semantic computing uses ontologies and semantic queries to understand relationships and context in data from various sources, helping to integrate information more easily.
- Together these tools could help with tasks like marketing and pricing optimization, fraud detection, faster claims processing, and more personalized
AI and Data Science Revolutionizing Industries and Shaping the Future
The document discusses how rapid advancements in artificial intelligence are disrupting industries globally. It outlines key developments in AI's history and applications that are streamlining tasks through automation, enabling personalized experiences and improved customer service, and poised to revolutionize healthcare. However, as AI becomes more prevalent, ethical and regulatory challenges also emerge regarding data privacy, bias, and other implications. The future potential of AI is limitless as it transforms additional sectors like transportation, education, energy, and the environment through applications such as autonomous vehicles.
Privacy in AI/ML Systems: Practical Challenges and Lessons LearnedKrishnaram Kenthapadi
How do we protect the privacy of users when building large-scale AI based systems? How do we develop machine learning models and systems taking fairness, accuracy, explainability, and transparency into account? Model fairness and explainability and protection of user privacy are considered prerequisites for building trust and adoption of AI systems in high stakes domains. We will first motivate the need for adopting a “fairness, explainability, and privacy by design” approach when developing AI/ML models and systems for different consumer and enterprise applications from the societal, regulatory, customer, end-user, and model developer perspectives. We will then focus on the application of privacy-preserving AI techniques in practice through industry case studies. We will discuss the sociotechnical dimensions and practical challenges, and conclude with the key takeaways and open challenges.
Artificial Intelligence For Digital Transformation PowerPoint Presentation Sl...SlideTeam
Presenting this set of slides with name - Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides. This PPT deck displays eighteen slides with in depth research. Our topic oriented Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Artificial Intelligence For Digital Transformation Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
This document provides an overview of artificial intelligence (AI), including its history, types, workings, applications, and pros and cons. It defines AI as the ability of computers to mimic human intelligence through tasks like problem-solving, learning, and decision making. The document outlines the major developments in AI from 1950 to present day. It describes the two main types of AI as narrow/weak AI that can perform specific tasks, versus general and super AI that may match or surpass human-level cognition. The key components and functionalities that enable AI are also summarized, along with its wide-ranging applications and the benefits and limitations of AI systems.
This document discusses artificial intelligence (AI) and its applications in business. It describes AI as the intelligence of machines and the branch of computer science that aims to create machine intelligence through techniques like neural networks, expert systems, and natural language processing. The document outlines how AI is used in various business functions like finance, marketing, human resources, manufacturing, and more to tackle complex problems, analyze data, optimize processes, and increase productivity. It also provides examples of specific AI applications in credit screening, forecasting, customer relationship management, and manufacturing scheduling.
UNLEASHING INNOVATION Exploring Generative AI in the Enterprise.pdfHermes Romero
The document provides an overview of generative AI, including its key concepts and applications. It discusses transformer models versus neural networks, explaining that transformer models use self-attention to capture long-range dependencies in sequential data like text. Large language models (LLMs) based on the transformer architecture have shown strong performance in natural language generation tasks. The document outlines the evolution of generative AI techniques from early machine learning to modern large pretrained models. It also surveys some commercial generative AI applications in industries like healthcare, finance, and gaming.
Algorithmic Bias: Challenges and Opportunities for AI in HealthcareGregory Nelson
Gregory S. Nelson, VP, Analytics and Strategy – Vidant Health | Adjunct Faculty Duke University
The promise of AI is quickly becoming a reality for a number of industries including healthcare. For example, we have seen early successes in the augmenting clinical intelligence for diagnostic imaging and in early detection of pneumonia and sepsis. But what happens when the algorithms are biased? In this presentation, we will outline a framework for AI governance and discuss ways in which we can address algorithmic bias in machine learning.
Objective 1: Illustrate the issues of bias in AI through examples specific to healthcare.
Objective 2: Summarize the growing body of work in the legal, regulatory, and ethical oversight of AI models and the implications for healthcare.
Objective 3: Outline steps that we can take to establish an AI governance strategy for our organizations.
The document discusses the challenges faced by corporate privacy departments and how they can better align with other business functions. It recommends that privacy departments find synergies with information security, product development, legal and other teams. It provides examples of how privacy can collaborate with different departments on tasks like product analysis, incident response and metrics. The document also outlines good practices for privacy programs, such as using recognized frameworks, conducting privacy assessments and demonstrating value through objective metrics.
GETTINGGDPR-READY MEANS SETTING UP A PRIVACY MANAGEMENT SYSTEM,
BEING ABLE TO SHOW IT AND KEEPING IT EFFECTIVE
A management system is a “living” entity which adapts to business context (new markets-products-services, M&A, demerge, law/policies changes, … ) and improves over time
IT Governance and Compliance: Its Importance and the Best Practices to Follow...GrapesTech Solutions
With new technology coming in every day, the need for IT governance and compliance is essential. IT governance and compliance are not only necessary for consumers but also for businesses. A strong IT governance plan can help add immense value to your business.
Many businesses are not aware of the importance of IT governance and Its Compliance. Hence it is important first to understand IT Governance and the Compliance Standards.
Explore the Significance of IT Governance and Compliance in 2024. Explore best practices for effective management, ensuring security, and meeting regulatory standards in the dynamic IT landscape.
Automatski is an IoT pioneer that addresses security and privacy concerns through its ground-up first principles IoT platform and standards compliance. It aims to eliminate reasons for customers to choose competitors by adhering to over a dozen security standards, including SAS 70, PCI DSS, Sarbanes-Oxley, ISO 27001, NIST, HIPAA, and the Cloud Security Alliance's CCM. Automatski was founded by technology experts with decades of experience and a track record of success with global Fortune 500 companies.
This document discusses how to successfully implement an IT security policy. It begins by defining what an IT security policy is - a written, ever-changing document that explains how an organization will protect its IT assets. It then outlines the importance of such policies for protecting data and controlling access. The document also discusses challenges across the seven domains of IT (user, workstation, LAN, etc.) and how policies can address each domain. It notes some potential barriers to implementation like human factors but emphasizes that successful policies are created, assigned responsibilities, ensure compliance, and are continually maintained. The overall goal is for policies to safeguard organizational data and resources from both internal and external threats.
This presentation explains Information Governance. Learn what it takes to improve the value of information, manage information risks, and reduce information costs.
Running head PROJECT PLAN INCEPTION1PROJECT PLAN INCEPTION .docxjeanettehully
Running head: PROJECT PLAN INCEPTION 1
PROJECT PLAN INCEPTION 2
Information Technology and Business
Babatunde Ogunade
CIS499: Information System Capston
Professor Reddy Urimindi
October 13, 2019
Information Technology and Business
Project Introduction
The very core operation of this company involves the collection and analysis of data through a currently limited technological infrastructure. The basis of this business may focus on leadership structure, the type of industry, business culture, core vision and mission including objectives. The company has a Chief Executive Officer (CEO) as the highest rank, four Information Technology experts and other employees. Marketing can, therefore, categorize this company as a service industry company with a core vision of a 60 percent growth in the next eighteen months and mission of redesigning its information technology to fulfill its organizational needs.
Product features, new market product, differentiation techniques, and value addition defines the type of business which the company is operating. The assessment of its product features which involves data indicate that the opportunities focus on marketing. In the continued operations of the company, the management is not foreseeing any shift from its original product but is rather fixing a differentiation technique within six months. An addition in product value should be achieved by employing an exclusively new technology based on a hybrid model, hosted solution or on-site solution.
The idea of integrating technologies from other partners to realize cost-effective outcomes and best operations outlines the outsourcing policies as far as new technology is concerned. Consequently, future intentions to acquire services such as Software-as-a-Service (SaaS) and cloud computing technologies may involve the adoption of knowledge and skills from outside the country, therefore, describing offshoring activities. As asserted by Aithal, (2017), the success of fulfilling the effective company operation, these activities are important.
One of the skilled personnel in the company is the Chief Information Officer (CIO) whose basic role is to keep a charge on the computer systems and information technology (IT) necessary in ensuring a company’s goals and objectives. Additionally, the CEO has devolved the responsibility of security protocols to the CIO in the process of more digitized frameworks. Other personnel includes the company CEO tasked with communicating to partners, creating the company mission and vision, and generally heading the implementation of both long term and short term objectives. The other information technician is mandated in both the installation and configuration of computer hardware and software.
Based on the current collection and analysis method, data on the customer, marketing, lifecycle, website engagement, and funnel analytics. In broad-spectrum, funnel analytics provide customer information through registration, ...
The document provides an overview of cybersecurity, explaining why it is important for businesses to implement security measures to protect their data, networks, and systems from cyber threats in order to avoid economic losses, reputational damage, and regulatory penalties. It discusses the components of cybersecurity including identity and access management, security information and event management, endpoint security, network security, and data security. The document also covers cybersecurity compliance regulations and best practices organizations should follow.
Unified Information Governance, Powered by Knowledge GraphVaticle
This document provides an overview of Infosys' Unified Information Governance solution powered by Knowledge Graph. It describes Infosys' vision to enable digital transformation for clients through an AI-powered core. The solution addresses challenges organizations face with complex system landscapes and data proliferation. It connects, observes, and provides sentient interaction with enterprise assets and data through a Knowledge Graph. This enables various roles to govern, manage, and consume information. Examples are provided of how the solution helps address priorities of specific roles like a CIO, CDO, and data scientist.
Bibek Chaudhary is interning in the GRC and IS Audit department. An IS audit examines an organization's information systems, processes, controls, and operations to determine if components are operating successfully to achieve organizational goals and objectives. IS audits can be undertaken as part of financial, internal, or other audits. Key areas covered in IS audits include systems and applications, information processing facilities, system development, IT management, and ensuring technical and operational controls. Major focuses of IS audits are governance and management of IT, information systems acquisition and development, protection of information assets, information systems operations and business resilience, and following appropriate audit methodologies.
The document discusses Information Security Management Systems (ISMS) and ISO/IEC 27001. It describes ISMS as a systematic approach to managing information security risks. ISO/IEC 27001 provides requirements for establishing, implementing, maintaining and improving an ISMS. It is based on a plan-do-check-act cycle. Implementing an ISMS and gaining ISO/IEC 27001 certification helps organizations manage information security risks, ensure legal and regulatory compliance, improve reputation, and gain a competitive advantage.
Information Governance, Managing Data To Lower Risk and Costs, and E-Discover...David Kearney
Information governance, records and information management, and data disposition policies are ways to help lower costs and mitigate risks for organizations. Policies and procedures to actively manage data are not just an IT "problem," they're a collaborative business initiative that is a must in today's "big data" environment. With electronic discovery rules, government regulations and the Sarbanes-Oxley Act, all organizations must proactively take steps to manage their data with well-governed processes and controls, or be willing to face the risks and costs that come along with keeping everything. Organizations must know what information they have, where it is located, the duration data must be retained and what information would be needed when responding to an event.
There have been numerous instances of severe legal penalties for organizations that did not have an electronic data strategy, tools, processes and controls to locate and understand their own data. In addition, the risks of unmanaged data include skyrocketing infrastructure and personnel costs and an increase in attorney time to manage massive amounts of data when a litigation event occurs.
Information governance is needed much like any business continuity and disaster recovery plans, but with an understanding of data: where data are located, how data are managed, event response, and regular testing of processes and procedures for preparedness.
ITIL (Information Technology Infrastructure Library) is a set of best practices for IT service management that covers processes such as incident management, problem management, change management, and availability management. By following ITIL frameworks and processes, organizations can better align IT services with business needs and ensure the proper delivery and support of technology services. The goal of ITIL is to help organizations improve efficiency, reduce costs, and become more responsive to business demands through standardized IT management practices.
Running Head CYBERSECURITY FRAMEWORK1CYBERSECURITY FRAMEWORK.docxhealdkathaleen
Running Head: CYBERSECURITY FRAMEWORK 1
CYBERSECURITY FRAMEWORK 5
Integrating NIST CSF with IT Governance Frameworks
Nkengazong Tung
University of Maryland University College
29 AUGUST 2019
IT governance is the processes that ensure the effective and efficient use of IT in enabling an organization to achieve its goals. In the eCommerce industry, IT governance develop structure by characterizing hierarchical detailing lines, oversight advisory groups, standards, approaches, and procedures. A well-characterized structure viably sets the working limits for the association (Moeller, 2017). It additionally sets guidelines by making or lining up with the corporate procedure and characterizing the short and long haul objectives for the association. In the eCommerce industry, it is important to note how the regulations are followed, how standards are followed by the process managers, how planning for the capacity of servers should be done, ensure all the IT assets are tracked, etc. This internal function that is self-checking the “health status” of the various process to ensure the smoother function is Governance. Comment by Michael Baker: Recommend subtitles that match rubric
IT management is overseeing IT services or innovation in an organization. It has several elements, all of which focus on aligning IT goals with business objectives in a way that creates the most value of an organization. These components are IT strategy, IT service and IT asset. Some of IT management issues faced by an eCommerce company include ways to secure customers information, providing value to the company, as well as supporting business operations. To address IT management challenges faced in eCommerce, IT policies must be put in place to define various processes within the organization. A policy is a set of guidelines that define how things are done within an organization. With a well-defined policy, activities in the eCommerce industry are well outlined and making it easy to operate.
Risk Management is the process used to identify, evaluate and respond to possible accidental losses in situations where the only possible outcomes are losses or no change in the status. It is an overall administration function that tries to evaluate and address the circumstances and end results of vulnerability and threat to an association (Susmann & Braman, 2016). The aim of threat management is to empower an association to advance towards its objectives and goals in the most immediate, proficient, and viable way. Risk management issues faced by an eCommerce company are loss of data, unauthorized access of data as well as system failure. To address risk management in the eCommerce industry, a comprehensive risk management plan must be developed to address possible risks that might cause harm to the system. A good risk management plan provides procedures as well as guideline on how to respond to threats and also unforeseen incidents. By having a well-laid plan, the ...
Access Insight provides identity and access intelligence by continuously analyzing relationships between identities, access rights, policies, resources and activities across an organization's systems. It identifies risks from misaligned user access and drives controls to manage that risk. Access Insight pulls identity and access data into its analytics engine to identify and prioritize risks, then displays this information on a dashboard to help users quickly modify access as needed and maintain continuous compliance.
Security architecture rajagiri talk march 2011subramanian K
The document discusses several topics related to cybersecurity and governance including:
- The need for dynamic laws to keep pace with rapid technological advancements in cyberspace.
- The absence of a single governing body and immature cybersecurity practices in many countries.
- A five-tier architecture model for cybersecurity consisting of data, process, technology, data management, and management architectures.
- The importance of information assurance over just information security to ensure availability, integrity and reliability of information systems.
- Key stakeholders in information assurance including boards of directors, management, employees, customers, and regulatory authorities.
Article started one year ago, obtains far more relevancy these days. Its meaning stays the same however: "Without laws and regulations would be chaos affecting our freedom and human nature."
The GDPR Most Wanted: The Marketer and Analyst's Role in ComplianceObservePoint
This eBook outlines the role marketers and analysts play in helping their companies:
- Govern all existing web and app technologies
- Collect, store and analyze data properly
- Ensure ethical marketing and analytics practices
This document discusses data privacy fundamentals and attacks. It begins with definitions of data privacy and the need to protect personally identifiable information. It then outlines common data privacy threats like phishing, malware, and improper access. The document also examines access control models and regulations around data protection. Overall, it provides an introduction to key concepts in data privacy and security risks to consider.
In this presentation we will look at the cause and effect of the problem, analyze preparedness and learn how you can better prepare, detect, respond and recover from cyber-attacks.
Receivership and liquidation Accounts
Being a Paper Presented at Business Recovery and Insolvency Practitioners Association of Nigeria (BRIPAN) on Friday, August 18, 2023.
Defending Weapons Offence Charges: Role of Mississauga Criminal Defence LawyersHarpreetSaini48
Discover how Mississauga criminal defence lawyers defend clients facing weapon offence charges with expert legal guidance and courtroom representation.
To know more visit: https://www.saini-law.com/
Synopsis On Annual General Meeting/Extra Ordinary General Meeting With Ordinary And Special Businesses And Ordinary And Special Resolutions with Companies (Postal Ballot) Regulations, 2018
सुप्रीम कोर्ट ने यह भी माना था कि मजिस्ट्रेट का यह कर्तव्य है कि वह सुनिश्चित करे कि अधिकारी पीएमएलए के तहत निर्धारित प्रक्रिया के साथ-साथ संवैधानिक सुरक्षा उपायों का भी उचित रूप से पालन करें।
Genocide in International Criminal Law.pptxMasoudZamani13
Excited to share insights from my recent presentation on genocide! 💡 In light of ongoing debates, it's crucial to delve into the nuances of this grave crime.
Lifting the Corporate Veil. Power Point Presentationseri bangash
"Lifting the Corporate Veil" is a legal concept that refers to the judicial act of disregarding the separate legal personality of a corporation or limited liability company (LLC). Normally, a corporation is considered a legal entity separate from its shareholders or members, meaning that the personal assets of shareholders or members are protected from the liabilities of the corporation. However, there are certain situations where courts may decide to "pierce" or "lift" the corporate veil, holding shareholders or members personally liable for the debts or actions of the corporation.
Here are some common scenarios in which courts might lift the corporate veil:
Fraud or Illegality: If shareholders or members use the corporate structure to perpetrate fraud, evade legal obligations, or engage in illegal activities, courts may disregard the corporate entity and hold those individuals personally liable.
Undercapitalization: If a corporation is formed with insufficient capital to conduct its intended business and meet its foreseeable liabilities, and this lack of capitalization results in harm to creditors or other parties, courts may lift the corporate veil to hold shareholders or members liable.
Failure to Observe Corporate Formalities: Corporations and LLCs are required to observe certain formalities, such as holding regular meetings, maintaining separate financial records, and avoiding commingling of personal and corporate assets. If these formalities are not observed and the corporate structure is used as a mere façade, courts may disregard the corporate entity.
Alter Ego: If there is such a unity of interest and ownership between the corporation and its shareholders or members that the separate personalities of the corporation and the individuals no longer exist, courts may treat the corporation as the alter ego of its owners and hold them personally liable.
Group Enterprises: In some cases, where multiple corporations are closely related or form part of a single economic unit, courts may pierce the corporate veil to achieve equity, particularly if one corporation's actions harm creditors or other stakeholders and the corporate structure is being used to shield culpable parties from liability.
Matthew Professional CV experienced Government LiaisonMattGardner52
As an experienced Government Liaison, I have demonstrated expertise in Corporate Governance. My skill set includes senior-level management in Contract Management, Legal Support, and Diplomatic Relations. I have also gained proficiency as a Corporate Liaison, utilizing my strong background in accounting, finance, and legal, with a Bachelor's degree (B.A.) from California State University. My Administrative Skills further strengthen my ability to contribute to the growth and success of any organization.
Business law for the students of undergraduate level. The presentation contains the summary of all the chapters under the syllabus of State University, Contract Act, Sale of Goods Act, Negotiable Instrument Act, Partnership Act, Limited Liability Act, Consumer Protection Act.
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https://veteranlegal.in/defense-lawyer-in-india/ | Criminal defense Lawyer in India has always been a vital aspect of the country's legal system. As defenders of justice, criminal Defense Lawyer play a critical role in ensuring that individuals accused of crimes receive a fair trial and that their constitutional rights are protected. As India evolves socially, economically, and technologically, the role and future of criminal Defense Lawyer are also undergoing significant changes. This comprehensive blog explores the current landscape, challenges, technological advancements, and prospects for criminal Defense Lawyer in India.
Sangyun Lee, 'Why Korea's Merger Control Occasionally Fails: A Public Choice ...Sangyun Lee
Presentation slides for a session held on June 4, 2024, at Kyoto University. This presentation is based on the presenter’s recent paper, coauthored with Hwang Lee, Professor, Korea University, with the same title, published in the Journal of Business Administration & Law, Volume 34, No. 2 (April 2024). The paper, written in Korean, is available at <https://shorturl.at/GCWcI>.
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4. Artificial Intelligence (“AI”) Regulation
A vast array of sets of AI principles have been published over the years as ethical guides to the
use of AI.
DEFINING AI
Artificial Intelligence is a term shaped by socio-behavioural rationales of human capabilities –
essentially, expectations that machines could emulate human cognition and behaviour. Artificial
intelligence is intelligence that machines display in the way they use data to solve problems
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5. AI and Intelligence
Intelligence in the world of AI is not merely book learning, a narrow academic skill, or test-taking
smarts. Rather, it reflects a broader and deeper capability for comprehending our
surroundings—‘catching on’, ‘making sense’ of things, or ‘figuring out’ what to do” (Gottfredson,
1997).
This characterises AI as a suite of technologies, exhibiting some degree of autonomous learning
and enabling: It does this through
● Pattern detection - by recognising regularities and irregularities in data
● Decision-making by generating rules from general data and apply specific profiles against
those rules
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6. Trustworthy AI
Trust is a principle that affects the use and adoption of technology across the world.
Trust affects how we perceive our data being used, the ability of technology to be used to make
decisions about ourselves which is accepted as not infringing on our rights and compromising on
security.
The EU recently released a paper on Trustworthy AI – aimed as a guide towards AI policy and
regulatory development. In this paper a series of principles underpinning trust in AI were
identified.
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7. Key Principles – Trustworthy AI
1. Lawfulness
2. Ethics
3. Accountability
4. Safety
5. Human Oversight
6. Diversity / Fairness
7. Transparency
8. Privacy
9. Non Discrimination
10. Societal wellbeing
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8. How is this relevant?
The key principles identified around the use of AI relate to factors that affect peoples ability to trust its usage.
They are also key to data design aimed at protecting personal data and complying with rules around personal data
processing.
These principles are interconnected and take into account the major risks and problems surrounding the use of
AI.
E.G – Privacy.
Privacy is important because all data relates to an identifiable thing and most times to identifiable persons. This
raises the need to balance the collection of information with the personal rights of the person whose information
is being collected and used. Forms on the internet all require the collection of a lot of personal information and
access to services require this as a matter of operational fact. As such it is an important principle to consider and
is often cited together with accountability and safety (trust) as the overarching principles surrounding the ethical
use of AI in today’s society.
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9. Societal Wellbeing
Autonomy is one of the key factors that affect the use and governance around AI today. Autonomy is important
because it determines a course of action. In the context of AI , autonomy is crucial because it affects how
decisions are made.
There are two modes of decision making in AI.
◦ Human Decision Making
◦ Automated Decision making
Autonomy around automated decision making affects how data is used or processed and is a key aspect to
regulations like the GDPR which protects basic rights around data usage.
Societal well being reflects the impact of AI use on society and therefore involves impact on rights such as privacy,
the ability of human to retain control of AI, the ethical use of AI and the ability to retain accountability around AI
usage. This are the areas around which AI is indirectly regulated under the auspices of data protection.
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10. Regulation
The UK financial services regulator requires that “A firm should establish and maintain appropriate systems and controls for managing
operational risks that can arise from inadequacies or failures in its processes and systems (and, as appropriate, the systems and processes
of third party suppliers, agents and others). In doing so a firm should have regard to:
(1) the importance and complexity of processes and systems used in the end-to-end operating cycle for products and activities (for
example, the level of integration of systems);
(2) controls that will help it to prevent system and process failures or identify them to permit prompt rectification (including pre-approval
or reconciliation processes);
(3) whether the design and use of its processes and systems allow it to comply adequately with regulatory and other requirements;
(4) its arrangements for the continuity of operations in the event that a significant process or system becomes unavailable or is destroyed;
and
(5) the importance of monitoring indicators of process or system risk (including reconciliation exceptions, compensation payments for
client losses and documentation errors) and experience of operational losses and exposures”.
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11. Compliance
Compliance in response to regulations like SYSC 13.7 of the FCA Handbook covered above means that
firms that use or are contemplating the use of AI need to be able to understand both the principles
underpinning its ethical use, key regulations affecting its use in operations and controls that mitigate
the key risks attached to its use.
For most firms this means having a strong grasp of their data management systems / infrastructure as
well as rules around the use of data generally where available.
The use of non personal data is mainly unregulated.
However personal data which exposes individuals to the possibility of breaches and right
infringements is increasingly regulated. Documents which assess risks to personal data and which
document how companies comply with regulations are key controls that mitigate the risk of
inappropriate systems and controls around the operational use of AI technology.
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13. DATA INPUT
AND OUTPUT
Output refers to the output or the finished
product after data is processed – it could be
websites, databases, platform. e.t.c
Input refers to what we put into the computers
that we use. This is usually in the form of data –
including personal data and operational data
(data needed to run a system – e.g alogirthms).
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14. What is a Data Management System?
A data management system refers to the infrastructure used to manage data in an organisation.
It includes the IT system, filing systems, the software used for data input and its outputs and all
of the controls used by a company to ensure that it meet legal requirements around the
processing of data and IT security.
Data management systems deal with infrastructure, data quality, and compliance or governance
around the data.
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15. INPUT AND DATA QUALITY
Input consists of data around objects, subjects, variable and operations.
This means that data needed to run a system can refer to any one of the above. As such to
ensure that the system has adequate data, there is a need to map out data requirements and
ensure that relevant data is made available to the system.
Simply put – you need the right input for the right output. This ensures accuracy in the system.
Data Quality : Deals with the accuracy, usefulness and breadth (representativeness) of the data
that you use. It deals with the question of whether the data is understandable, easy to refer to
and is extensive enough to cover the areas it needs to cover to make it fit for purpose.
Issues around data quality touch on ideas such as data labelling, diversity and data design.
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17. Data Compliance Infrastructure
This refers to the infrastructure that supports the data management system. It includes the IT
systems, people who run the systems and the rules that impact the system and ways in which
the system is run to make it accountable – such as
Software
Hardware
Policies
Procedures
Regulatory risk assessments
Governance
Data protection officer
Data Protection teams (Analysts / Managers / Data Protection Officer (DPO)
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18. Governance
Governance is all the processes of interaction be they through the laws, norms, power or language of an
organized society is imposed on and regulated within a social system.
In the world of AI – software creation and programming is not regulated however the use of personal data is
increasingly subject to regulation. Software development while unregulated is subject to assurance.
Regulation : Within the EU – personal data is protected by the GDPR (Data Protection Act 2018 in the UK). This
brings it within the regulatory scope of all companies – where it is usually covered by data protection teams.
Assurance : Software testing - Software Testing helps find and fix already existing mistakes and Quality Assurance
helps avoid them. Both of these processes are important and if you want to meet the best quality criteria, you
cannot have one without the other. This is a form of governance around the use of AI as – AI enabled software
can be developed and tested to ensure that it is works and meets operational principles and guidelines.
Governance focuses on data quality assurance and data compliance.
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19. The key questions to ask around data use
Who uses the data ? – this question reveals the data controller (data owner) and processor
(user).
When and where is it used? – this question deals operationally with the issues of time and
jurisdiction. Jurisdiction determines the law affecting the processing of personal data.
Why is it used? – this question refers to the legality of usage – the reason needs to be legal and
fall within the legal basis of data processing such as consent and legitimate interest.
How is it used ? – this question touches on data processing principles such as data accuracy,
minimalization of data use and the protection of personal data through data rights for subjects
of data processing.
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20. Data Quality / Data Compliance
Data quality
◦ This refers to how fit for purpose the data held by a controller is.
◦ Assurance systems such as data quality assurance
◦ Software testing
◦ Data design principles
◦ Data quality policies
Data Compliance
◦ Refers to the meeting of regulatory rules and requirements.
◦ Governance processes such as data protection risk reviews – e.g. DPIA’s
◦ Data protection policies and infrastructure aimed at upholding data rights and meeting data protection principles.
◦ Regulations protecting the processing or use of data – eg personal data.
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21. Data rights
Focus on principles that resonate both around AI trustworthiness and its ethical use as well as personal
data protection. These rights within the GDPR are
◦ the right to be informed about the collection and the use of their personal data
◦ the right to access personal data and supplementary information
◦ the right to have inaccurate personal data rectified, or completed if it is incomplete
◦ the right to erasure (to be forgotten) in certain circumstances
◦ the right to restrict processing in certain circumstances
◦ the right to data portability, which allows the data subject to obtain and reuse their personal data for their own
purposes across different services
◦ the right to object to processing in certain circumstances
◦ rights in relation to automated decision making and profiling
◦ the right to withdraw consent at any time (where relevant)
◦ the right to complain to the Information Commissioner
◦ The right to be informed
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22. Corresponding AI principles
Transparency : This involves the data protection principles of purpose limitation, data minimisation and data storage minimisation. Data right such as
the right to be informed, right to access personal data and the rights to erasure / object to processing - all come within the principle to transparency as
as they are all designed to aid transparency within the use of personal data.
Accountability : This involves the data protection principle that makes controllers and processors accountable for the personal data that they process.
Corresponding data rights – include the rights to restrict processing, access processed data, rights in relation to automated decision making and all of
of these are underpinned by the data protection principle that requires a lawful basis for the protection of personal data.
Safety : This involves the security data protection principle such as that in the GDPR which states that “ you must ensure that you have appropriate
security measures in place to protect the personal data you hold”. This requires that breaches of data protection such as unauthorised use of data,
unauthorised access to data, and hacking are reported to data regulators. Companies can be fined for breaches of personal data protection thus
ensuring that they are more likely to comply with regal requirements. Safety measures around the protection of personal data include the use of data
data protection risk / impact assessments , Data protection policies, a clear desk policy, cyber / antivirus protection. IT security policies.
In conclusion, much of the input that goes into AI and therefore, AI itself remains unregulated . However, personal data protection means that there is an element of
governance which affects the use of AI today. Data protection is largely where AI meets Compliance in todays regulated world. There have been many arguments for
the governance of AI itself – with commentators putting forth arguments such as AI itself being ungovernable – and it usage being the are subject to governance.
While there is agreement on key principles around trustworthiness and the ethical use of AI this is a changing space which could be subject to governance in the near
future.
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