DADD: Discovering and Attesting Digital Discrimination

In digital discrimination, users are treated unfairly, unethically or just differently based on their personal data. Examples include low-income neighborhoods targeted with high-interest loans; women being undervalued by 21% in online marketing; and online ads suggestive of arrest records appearing more often with searches of black-sounding names than white-sounding names. Digital discrimination very often reproduces existing instances of discrimination in the offline world by either inheriting the biases of prior decision makers, or simply reflecting widespread prejudices in society. Digital discrimination may also have an even more perverse result, it may exacerbate existing inequalities by causing less favourable treatment for historically disadvantaged groups, suggesting they actually deserve that treatment. As more and more tasks are delegated to computers, mobile devices, and autonomous systems, digital discrimination is becoming a huge problem.

Digital discrimination can be the result of algorithmic biases, i.e., the way in which a particular algorithm has been designed creates discriminatory outcomes, but it also occurs using non-biased algorithms when they are fed or trained with biased data. Research has been conducted on so-called fair algorithms, tackling biased input data, demonstrating learned biases, and measuring relative influence of data attributes, which can quantify and limit the extent of bias introduced by an algorithm or dataset. But, how much bias is too much? That is, what is legal, ethical and/or socially-acceptable? And even more importantly, how do we translate those legal, ethical, or social expectations into automated methods that attest digital discrimination in datasets and algorithms?

DADD (Discovering and Attesting Digital Discrimination) is a *novel cross-disciplinary collaboration* to address these open research questions following a continuously-running co-creation process with academic (Computer Science, Digital Humanities, Law and Ethics) and non-academic partners (Google, AI Club), and the general public, including technical and non-technical users. DADD will design ground-breaking methods to certify whether or not datasets and algorithms discriminate by automatically verifying computational non-discrimination norms, which will in turn be formalised based on socio-economic, cultural, legal, and ethical dimensions, creating the new *transdisciplinary field of digital discrimination certification*.

DADD will run a programme of activities with a view to maximising:

*Long-term cultural change* will be pursued by: i) actively engaging with the four research themes in King's Research Strategy and Vision 2029 to foster and encourage cross-disciplinary research in the College, and in particular, DADD is part of and will work closely with the Social Justice theme (one of the four themes); ii) building project team's skills (PDRAs and investigators), leveraging the co-creation and the way of working together in the project, and KCL's Centre for Research Staff Development and KCL's Engagement Services Team; and iii) training the next generation of digital discrimination researchers and professionals (PDRAs, PhD and MSc students), leveraging the PhD studentship committed by KCL, the London Interdisciplinary Social Science Doctoral Training Partnership, and KCL MSc programmes like the MSc in Data Science.

*Influencing other ICT researchers to foster adoption of cross-disciplinarity and co-creation* will be delivered by organising workshops at academic conferences, at King's and in conjunction with the Science and Engineering South (SES), and at other venues in collaboration with other projects funded in this call, together with publications (magazines like CACM) on cross-disciplinary and co-creation methodologies, their benefits, and know-how gained throughout the project.

*Sustainable economic and public sector impact* will be delivered by working directly with companies and the public sector (e.g. e-government) using a two-staged approach. In the first stage, a business and public-sector-focused workshop will run to engage with potentially interested businesses and professionals, seeking engagement with the Digital Catapult. We have already secured support and collaboration from Google and AI Club who will also be invited to these events. In the second stage, activities with already secured partners and other selected partners from stage one will run, e.g., Google confirmed that they will collaborate with DADD (advisory board and project activities). DADD will utilise internal (IAA) and external (KTP, Google schemes) grant mechanisms for follow-up collaborations beyond its life, and it will engage with KCL's IP & Licensing Team to explore commercialization options for the Toolkit.

*Influencing policy and regulation on digital discrimination* will leverage the extensive expertise at KCL through its Policy Institute to reach out to and engage with policy makers, and campaign for and raise awareness about digital discrimination, to update them with project findings, and explore possible areas of impact in current and prospective regulations. In particular, relevant regulation bodies like the Information Commissioner's Office (ICO) and Prudential Regulation Authority (PRA) will be contacted and invited to engagement workshops. Also, the PI will leverage his policy contacts as member of the Policy Fellows Network at Centre for Science and Policy, University of Cambridge (a network of academics and policy professionals).

*Raising public awareness of digital discrimination* is embedded in the participatory research methods used in DADD in which the general public will take part (e.g. techno-cultural workshops). Public Awareness will also be delivered by means of a highly active Social Media and Press Releases strategy.