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How Transparent AI Will Enable More Equitable Products

How Transparent AI Will Enable More Equitable Products

70% of AI leaders cannot explain how specific AI model decisions or predictions are made, and only 35% said their organization made an effort to use AI in a way that was transparent and accountable. Ethics and AI have become a central conversation in the tech industry, driven by the lack of understanding of data models, what information they are trained on, and the risk of bias. This is especially critical in sectors like healthcare, where algorithmic bias can leave out significant portions of a population and lead to devastating results. Attendees of this session will: - Understand the inherent challenges in utilizing artificial intelligence in a way that is transparent and accountable. - Why you need to pay more attention to your data models and how you are are using & training AI and deep learning. - How to develop a framework for identifying and overcoming inherent biases in data sets to ensure that your AI is driving more equitable products.

70% of AI leaders cannot explain how specific AI model decisions or predictions are made, and only 35% said their organization made an effort to use AI in a way that was transparent and accountable. Ethics and AI have become a central conversation in the tech industry, driven by the lack of understanding of data models, what information they are trained on, and the risk of bias. This is especially critical in sectors like healthcare, where algorithmic bias can leave out significant portions of a population and lead to devastating results. Attendees of this session will: - Understand the inherent challenges in utilizing artificial intelligence in a way that is transparent and accountable. - Why you need to pay more attention to your data models and how you are are using & training AI and deep learning. - How to develop a framework for identifying and overcoming inherent biases in data sets to ensure that your AI is driving more equitable products.

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How Transparent AI Will Enable More Equitable Products

  1. 1. Building a Vertical SaaS company in a Horizontal SaaS world Dipanwita Das, CEO, Sorcero August 2022 1
  2. 2. Agenda 1 2 3 4 5 6 2 Introductions The Vision for Vertical SaaS Sorcero’s Journey Pros & Cons of Vertical SaaS Vertical SaaS & AI: A Perfect Match for the Enterprise Surprises and Learnings
  3. 3. Introducing Sorcero and my journey 3 Challenge of Disseminating Complex Public Health Data in 20 countries to stakeholders + World’s medical knowledge in hardest data format: unstructured technical language = Our mission: to improve patient outcomes through effective use of the world’s knowledge.
  4. 4. The Evolution of SaaS 4 Vertical SaaS companies sell comprehensive, cross-functional mission-critical technology to one industry. Horizontal SaaS companies do a few things many organizations need and are valued based on a massive, total addressable market.
  5. 5. Enterprise Horizontal SaaS vs. Cloud Platforms 5 Broadly applicable enterprise software uses cases are continuously built and deployed. Horizontal SaaS entrants compete with 1,000s of Cloud apps consumable via credits. AWS Azure GCP
  6. 6. The Vision of Vertical SaaS Focus: A software company focussed on owning a single market with mission-critical products, rather than small shares across a number of markets. Win: Only one or two companies can dominate the vertical market, trading breadth for market share and depth. Expand: The best vertical SaaS have a layer cake approach to product and always have a product suite. Extend: Vertical SaaS develops their second product years before the first product slows down. 6 Thanks to Dave Yuan at Tidemark
  7. 7. Sorcero: How did we get here? 7 The challenge with growing unstructured healthcare content Tested 3 markets ● Life Sciences ● Insurance ● Healthcare Increasing data regulations and data needs in Life Sciences Breakthroughs in large language AI Discovery: Medical Affairs in Life Sciences owns Scientific & Clinical Data delivery to Healthcare & Insurance in a $11 trillion market
  8. 8. Enterprise health data is vast and messy, perfect for domain-specific AI learning unstructured data types 8 DATA PREVALENCE
  9. 9. 9 Sorcero focused on eliminates manual reporting, bringing to bear powerful analytical tools via Clarity platform workflows and AI Analytics Clinical Trials Congresses Social Media Clinical Data Medical Guidelines Publications EXTERNAL MEDICAL CONTENT CRM Data Advisory Boards Medical Inquiries & Responses Medical Education Materials KOL / HCP Profiles ENTERPRISE MEDICAL CONTENT Field Medical Publications Teams Medical Information Health Economics (HEOR) Medical Analytics Senior Leadership MEDICAL FUNCTIONS Medical Education Non-Registrational Clinical Trials (nRE)
  10. 10. Vertical SaaS companies deliver value to their market by serving mission-critical industry specific workflows. Domain-specific AI focuses on delivering precision processing of industry-specific data, learning specific workflows for insights. Enterprise AI and Vertical SaaS becomes an inextricable part of their customer’s value chain, with domain-specific workflows and data processing that unlocks messy enterprise data to augment mission-critical decisions. 10 Vertical SaaS & AI: A Perfect Match for the Enterprise
  11. 11. Deploy AI alongside Expert Workflows to get Enterprise Adoption 11 Example - AI in Pharmacovigilance: Sorcero models achieve very high accuracy, but if a single new side-effect is missed, it can significantly impact health outcomes. At Sorcero, we shifted the focus from scoring to enhance customer’s workflow to identify the relevant documents faster and less effort - reducing human error, not full automation.
  12. 12. 12 Surprise and Learnings Life Sciences is incredibly ahead and behind other industries in adopting technology. Vast investments in AI drug discovery, little in efficiency. Lean into “weak” spots. Domain-specific AI can achieve much higher accuracy that general-domain AI, due to targeted problem space. Vertical SaaS is still not understood by many investors that are still focuses on “unencumbered growth”. Go Global Fast many verticals have major clusters outside of the United States hungry for technology and willing to invest. Pick a Vast Vertical Life Sciences is the highest-margin business in the world, with $2.4 trillion in revenue. Healthcare is $11 trillion. Markets are massive in depth.
  13. 13. Language Intelligence for Life Sciences ∵ ∑ (human + AI) > human ⊻ AI 13

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