Episode 5 - AI and the Law Podcast: AI and discrimination
Jul 5, 2023
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Professor Catherine Barnard, Professor of EU and Employment Law at the University of Cambridge, discusses discrimination claims in the context of AI. They explore existing case law on algorithmic determinations and potential defences and liability by companies. They also discuss the new proposal for an EU Platform Work directive and regulation of algorithmic performance measures.
AI decision-making can lead to discrimination in various ways, such as facial recognition software inaccurately identifying people with black skin, resulting in unfair treatment.
It is important to understand direct and indirect discrimination in the context of AI, recognizing the different types of discrimination and corresponding legal defenses to combat AI-based discrimination effectively.
Deep dives
AI and Discrimination
AI decision-making can lead to discrimination in various ways. For instance, facial recognition software has been found to have difficulty accurately identifying people with black skin, which can result in unfair treatment, such as denying boarding passes or wrongful arrests. Similarly, in the EU settlement scheme, the use of AI to determine eligibility may discriminate against individuals with irregular employment patterns or from low-income backgrounds. Another example is in money lending or mortgages, where AI-based decisions could discriminate against those who are not married but in civil partnerships. It is crucial to examine how AI decision-making intersects with discrimination law and the challenges it poses.
Direct and Indirect Discrimination
In the context of AI, it is important to understand direct and indirect discrimination. Direct discrimination occurs when individuals are treated less favorably due to their protected characteristics, while indirect discrimination refers to rules or practices that appear neutral but disproportionately affect certain groups. For instance, a height requirement for firefighter jobs may indirectly discriminate against women or certain ethnic minority groups. It is essential to recognize the different types of discrimination and the corresponding legal defenses available, such as objective justification for indirect discrimination, to combat AI-based discrimination effectively.
Responsibility and Transparency
Determining liability for AI-based discrimination raises complex challenges. While third-party developers may create the algorithms, it is often the employers or service providers who provide the data and have control over their use. To reduce exposure to discrimination liability, it is crucial for employers to take proactive steps, such as conducting impact assessments and demanding transparency from third parties. Employers must ask difficult questions and ensure that practical measures are in place to prevent bias and discrimination within AI systems. As AI and algorithms become more prevalent, the legal landscape will need to evolve to address the unique challenges they present.
In this episode Professor Catherine Barnard, Professor of EU and Employment Law at the University of Cambridge discusses with Katherine Apps KC how discrimination claims could be litigated in the context of the use of AI. Catherine discusses existing case law on algorithmic determinations and how the elements of a discrimination claim could be demonstrated by a claimant or organisation. Catherine and Katherine also discuss potential defences and liability by companies and public authority for contractors’ software and how this is likely to be approached. Catherine also discusses the new proposal for an EU Platform Work directive and the structure used for regulation of algorithmic performance measures and whether it could become a blueprint used in other areas.
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