Building and using AI systems fairly can be challenging, but is hugely important if the potential benefits from better use of AI are to be achieved.
Recognising this, the government’s recent white paper "A pro-innovation approach to AI regulation" proposes fairness as one of five cross-cutting principles for AI regulation. Fairness encompasses a wide range of issues, one of which is avoiding bias, which can lead to discrimination.
This issue has been a core focus of CDEI since we were established in 2019. Our 2020 Review into bias in algorithmic decision making set out recommendations for government, regulators, and industry to tackle the risks of algorithmic bias. In 2021, we published the Roadmap to an Effective AI Assurance Ecosystem, which explores how assurance techniques such as bias audit can help to measure, evaluate and communicate the fairness of AI systems.
Over this period, this issue has received an increasingly strong focus across industry, academia and government, with significant numbers of academic papers and developer toolkits emerging. However, organisations seeking to address these challenges in real world examples continue to face a range of challenges, including:
- Lacking access to the demographic data they need to identify and mitigate unfair bias and discrimination in their systems.
- Understanding how to usefully apply a complex range of statistical notions of bias to understand the fairness of real world outcomes in their particular context.
- Ensuring that any bias mitigation techniques used are themselves ethical and legal in the UK context.
CDEI’s Fairness Innovation Challenge
To help address some of these challenges, CDEI plans to run a Fairness Innovation Challenge to support the development of novel solutions to address bias and discrimination across the AI lifecycle. The challenge also aims to provide greater clarity about which assurance tools and techniques can be applied to address and improve fairness in AI systems, and encourage the development of holistic approaches to bias detection and mitigation, that move beyond purely technical notions of fairness.
This challenge will build on our experience running the recent Privacy Enhancing Technologies Prize Challenges (in collaboration with the US government), which brought together industry, academia, government and regulators to help drive technical innovation in a real world context.
The Challenges
The challenges described above are much broader than technical ones, and we are keen to ensure that participants in the challenge are developing holistic solutions to address fairness challenges. Regulators play a key role in this area, and we’re delighted that The Equality & Human Rights Commission (EHRC) and The Information Commissioner’s Office (ICO) have agreed to support the challenges. They will help guide participants through some of the legal and regulatory issues, as well as using learnings from the challenge to shape their own broader regulatory guidance on these issues.
Call for Use Cases
As we finalise the design and scope of this challenge, we are eager to hear from you on how we can shape it to be most effective. In particular, we are keen to identify real world use cases which could form the basis of specific challenge projects. We are today launching a call for use cases, and would welcome submissions of specific fairness-related problems faced by organisations designing, developing, and/or deploying AI systems.
Conclusion
The Fairness Innovation Challenge is an opportunity for organisations to develop innovative solutions to address fairness challenges in AI systems. We believe that by working together, we can drive progress towards a future where AI is used in a way that is fair and beneficial to all.
FAQs
Q: What is the Fairness Innovation Challenge?
A: The Fairness Innovation Challenge is a competition aimed at developing novel solutions to address bias and discrimination in AI systems.
Q: Who is eligible to participate?
A: The challenge is open to organisations of all sizes and types, including industry, academia, and government.
Q: What are the challenges facing organisations in addressing fairness in AI systems?
A: Organisations face a range of challenges, including lacking access to demographic data, understanding how to apply statistical notions of bias, and ensuring that bias mitigation techniques are ethical and legal.
Q: What is the role of regulators in the challenge?
A: Regulators, including the EHRC and ICO, will provide guidance on legal and regulatory issues, and use learnings from the challenge to shape their own broader regulatory guidance on fairness in AI systems.
Q: How can I submit a use case for the challenge?
A: You can submit a use case by using the Google form linked here.
