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Responsible AI: Hype vs. Reality and Designing Responsibly at Every Stage of AI Responsible AI: Hype vs. Reality and Designing Responsibly at Every Stage of AI
AI is increasingly being applied to business-critical use cases at companies across multiple industries, but the highest-performing algorithms are often black boxes, leading to... Responsible AI: Hype vs. Reality and Designing Responsibly at Every Stage of AI

AI is increasingly being applied to business-critical use cases at companies across multiple industries, but the highest-performing algorithms are often black boxes, leading to a lack of transparency. This has led to a growing number of high-profile cases of allegedly biased AI algorithms in industries ranging from banking and healthcare to functions like hiring; and the negative impact on actual human lives is far-reaching. Businesses, consumers, and regulators are consequently calling for more transparency and visibility into AI solutions. 

Given this, many businesses are increasingly turning towards building AI systems with a focus on responsible development. Responsible AI, as a concept, is becoming top of mind for multiple businesses as they embark on high-impact and critical AI projects. Responsible AI is a broad term with multiple definitions depending on the company or organization. At a minimum, it is the practice of building AI that is transparent, accountable, ethical, and reliable. When AI is developed responsibly, stakeholders have insight into how decisions are made by the AI system, the system is governable and auditable through human oversight, outcomes are fair to its end users, stakeholders have visibility into AI post-deployment, and the AI system continuously performs as expected in production i.e. fairness is maintained and models are high-performant.

In order to build and implement a Responsible AI practice, there are different elements to consider:

  • Transparency: providing visibility to key stakeholders on decisions made by AI systems including explanations and a deep level of understanding behind these decisions
  • Accountability: the importance of holding AI accountable for the decisions made with checks and balances, regular validation checkpoints, and much-needed human-oversight
  • Ethics: broadly, this relates to human well-being with AI-made decisions or outcomes. Ensuring that outcomes and predictions from AI systems are ethical, fair, and inclusive
  • Reliability: AI models can degrade over time resulting in anomalies that might be considered unethical or otherwise have a negative impact on the end-users or the business. Ensuring continuous high-performance, whether detecting decay in models or data, is critical to building responsibly

Responsible AI is becoming top of mind for multiple businesses as they embark on high-impact and critical AI projects. To enable these individuals to build better by making their AI systems more transparent and accountable, Fiddler Labs is hosting a panel of Responsible AI experts from Facebook, Microsoft, and Hired to discuss the best ways to build responsible, accountable, and transparent AI. 

Join us July 2nd at 11am PST!

Article by Anusha Sethuraman, Head of Product Marketing, Fiddler Labs – www.fiddler.ai

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