AI and Social Justice: From Avoiding Harms to Positive Action

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AI systems are increasingly embedded in society and impact many areas like criminal justice, surveillance, and financial decisions. This raises issues of social justice, fairness, and equity that must be addressed. The document discusses four areas to focus on: access to AI systems and data; ensuring systems are developed responsibly; establishing policies around AI and social justice; and avoiding harm through techniques like auditing models and using inclusive data. Overall, the document argues that achieving social justice will require addressing biases in existing systems, being aware of limitations of algorithms, and involving diverse stakeholders.

Technology

AI and Social Justice
Clara Crivellaro
Open Lab,
Newcastle University, Newcastle, UK
Alan Dix
Computational Foundry,
Swansea University, Wales, UK
https://alandix.com/ai4sj/
@alanjohndix
@unsolopunto

AI … broad (e.g. EU defn.)
big data
statistics
ML and NN
classic AI

Social Justice
equity
fairness
empowerment

AI is embedded in society
US judges’ parole decisions
China mass face recognition
Autonomous weapons
Loan and insurance decisions
France was ruled by AI in 2019!

Avoid harm
• Bans
• Inclusive data sets
• Auditing
• Base rates and
correlates
• Neutral is not unbiased

ACCESS
DOING
IT RIGHT
POLICY
DOING
IT RIGHT

UK A’level ‘mutant algorithm’
• Entrenching
inequality
• Tried to be fair
• Lack of data
• Lack of trust
• Unreasonable
expectations of
algorithms
• Mirror on existing
system?

context and
system
AI
Richard Foster CC BY-SA 2.0 https://en.wikiversity.org/wiki/File:Different_people.jpg
David Iliff CC BY-SA 3.0 https://en.wikipedia.org/wiki/File:Hopetoun_falls.jpg
Michael Janich CC BY-SA 3.0 https://commons.wikimedia.org/wiki/File:Hongkong_central_kowloon-full.jpg

Human Rights Information
and
Documentation
Systems
https://huridocs.org/
https://www.uwazi.io/

Explore the ethical landscape of Artificial Intelligence (AI) through our insightful PowerPoint presentation. Delve into crucial considerations that shape the responsible development and deployment of AI technologies. From privacy concerns and bias mitigation to transparency and accountability, this presentation covers the key ethical dimensions of AI. Gain a comprehensive understanding of the ethical challenges and solutions in the rapidly evolving world of artificial intelligence. Stay informed and empower your audience with the knowledge needed to navigate the ethical intricacies of AI responsibly. Let us see the good and bad effects of the impact of Artificial Intelligence and the emerging technologies!

Making sense of big data

bis_foresight

AI and its Effects on the Global World Order

The Future Society

Like electricity or the steam engine, Artificial Intelligence (AI) is a true general purpose technology: It can be used to drive economic gains, but also to project hard and soft power. Its widespread adoption will irrevocably change the international order as its effects on welfare, trade and defense transcend national boundaries. In this keynote speech, Simon Mueller, Expert on AI Governance and Executive Director of the AI Initiative of The Future Society, will provide perspective on the range of issues, speak about current dynamics and discuss options to address emerging challenges.

Presentation from Prof. Dr. Max Welling, Professor of Machine Learning at the University of Amsterdam, at Textkernel's Intelligent Machines and the Future of Recruitment on June 2nd in Amsterdam. At the end of this slide deck, you can also find the YouTube recording. Due to increased compute power and large amounts of available data, machine learning is flourishing once again. In particular a technology called deep learning is making great strides maturing into a powerful technology. Max Welling briefly discusses variants of deep learning, such as convolutional neural networks and recurrent neural networks. But what lies around the corner in machine learning? He will discuss the three developments that in his opinion will become increasingly important: 1) Learning to interact with the world through reinforcement learning, 2) Learning while respecting everyone's privacy, and 3) Learning the causal relations in data (as opposed to discovering mere correlations). Together, they represent the "power tools" of the future machine learner.

[Slides] What Do We Do with All This Big Data by Altimeter Group

Altimeter, a Prophet Company

Slides for Altimeter's webinar: What Do We Do with All This Big Data? Fostering Insight and Trust in the Digital Age Watch the webinar replay at: http://www.slideshare.net/Altimeter/webinar-what-do-we-do-with-all-this-big-data-altimeter-group Download the report at: http://pages.altimetergroup.com/what-do-we-do-with-all-this-big-data-report.html Description: Big data has been called one of the biggest disruptions of the digital age. To make the best use of data, executives need to educate themselves on how to extract insight from data in a way that engenders trust — and use this insight to plan their data strategy now.

AI Ethics and Implications For Developing Societies.pptx

Ishaku Gayus Bwala

The presentation delves into several remarkable breakthroughs that artificial intelligence (AI) has achieved across diverse domains, including healthcare, medicine, gaming, and the creative arts. These breakthroughs underscore the profound impact that AI is making on these sectors, revolutionizing the way we approach medical diagnostics, enhancing interactive gaming experiences, and even inspiring new forms of artistic expression. In these accomplishments, the presentation also shines a spotlight on the potent ethical challenges that AI presents. As AI systems become increasingly integrated into our daily lives, questions about privacy, bias, accountability, and transparency become ever more pressing. The implications of AI's unethical use are far-reaching, potentially affecting not only individuals but society as a whole. Towards the end of the presentation, a compelling visual representation is shared: a chart that maps out the progress of 70 countries in developing policies, strategies, and regulations for the ethical use of AI. This chart provides a global perspective on how different nations are addressing the ethical considerations surrounding AI. It highlights the varying degrees of preparedness and commitment among countries to ensure that AI technologies are harnessed for the greater good while safeguarding against misuse, with developing countries lagging far behind By exploring these AI breakthroughs and ethical dilemmas, and by examining the global landscape of AI governance, the presentation offers a comprehensive view of AI's transformative potential and the collective responsibility we share in guiding its ethical evolution.

Better Data for a Better World

Rothamsted Research, UK

How open data contribute to improving the world. The life science use case. The technical, social, ethical issues. This was a talk given within the iGEM 2020 programme by the London Imperial College students group (https://2020.igem.org/Team:Imperial_College), in a webinar organised by the SOAPLab group on the topic of Ethics of Automation. Excellent Dr Brandon Sepulvado was the other speaker of the day.

The age of analytics

bis_foresight

ACCA Version of AI & Healthcare: An Overview for the Curious

KR_Barker

This is the version of my AI & Healthcare class that I presented to attendees of the Association of Cancer Center Administrators' 2024 annual conference in Philadelphia. AI is widely utilized in healthcare. This presentation provides a friendly introduction to the topic for librarians, health professionals, and anyone with an interest in the topic. Attendees will come away informed about the field’s history, conversant with definitions of important concepts, an understanding of how AI can become biased (and what that means for patients), and familiar with some of the many ways that AI is currently being used in healthcare.

The Future is in Responsible Generative AI

Saeed Al Dhaheri

Bias in algorithmic decision-making: Standards, Algorithmic Literacy and Gove...

Ansgar Koene

Big Data in Healthcare and Medical Devices

PremNarayanan6

Data & Digital Ethics - CDAO Conference Sydney 2018

Kate Carruthers

Webinar slides sept 23 2021 mary aiken

CapitolTechU

Big Data: Opportunity & Challenges

Kuncoro Wastuwibowo

Big Data: Opportunity & Challenges

Kuncoro Wastuwibowo

Towards Explainable Fact Checking (DIKU Business Club presentation)

Isabelle Augenstein

Outline: - Fact checking – what is it and why do we need it? - False information online - Content-based automatic fact checking - Explainability – what is it and why do we need it? - Making the right predictions for the right reasons - Model training pipeline - Explainable fact checking – some first solutions - Rationale selection - Generating free-text explanations - Wrap-up

Making Decisions in a World Awash in Data: We’re going to need a different bo...

Micah Altman

In his abstract, Scriffignano summarizes as follows: l explore some of the ways in which the massive availability of data is changing and the types of questions we must ask in the context of making business decisions. Truth be told, nearly all organizations struggle to make sense out of the mounting data already within the enterprise. At the same time, businesses, individuals, and governments continue to try to outpace one another, often in ways that are informed by newly-available data and technology, but just as often using that data and technology in alarmingly inappropriate or incomplete ways. Multiple “solutions” exist to take data that is poorly understood, promising to derive meaning that is often transient at best. A tremendous amount of “dark” innovation continues in the space of fraud and other bad behavior (e.g. cyber crime, cyber terrorism), highlighting that there are very real risks to taking a fast-follower strategy in making sense out of the ever-increasing amount of data available. Tools and technologies can be very helpful or, as Scriffignano puts it, “they can accelerate the speed with which we hit the wall.” Drawing on unstructured, highly dynamic sources of data, fascinating inference can be derived if we ask the right questions (and maybe use a bit of different math!). This session will cover three main themes: The new normal (how the data around us continues to change), how are we reacting (bringing data science into the room), and the path ahead (creating a mindset in the organization that evolves). Ultimately, what we learn is governed as much by the data available as by the questions we ask. This talk, both relevant and occasionally irreverent, will explore some of the new ways data is being used to expose risk and opportunity and the skills we need to take advantage of a world awash in data.

A flexible QR-code infrastructure for heritage

AI and Social Justice: From Avoiding Harms to Positive Action

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