The use of artificial intelligence (AI) presents risks to equality, potentially embedding bias and discrimination. Auditing tools are often promised as a solution. However our new research, which examines tools for auditing AI used in recruitment, finds these tools are often inadequate in ensuring compliance with UK Equality Law, good governance and best practice. We argue in this report that a more comprehensive approach than technical auditing is needed to safeguard equality in the use of AI for hiring, which shapes access to work. Here, we present first steps which could be taken to achieve this. We also publish a prototype AI Equality Impact Assessment which we plan to develop and pilot.

A growing group of lawyers are uncovering, navigating, and fighting the automated systems that deny the poor housing, jobs, and basic services. Increasingly, the fight over a client’s eligibility now involves some kind of algorithm. “In some cases, it probably should just be shut down because there’s no way to make it equitable,”

Racism is a systemic issue that pervades every aspect of life in the United States and around the world. In recent months, its corrosive…

Many of the issues discussed about data & racial inequality have been the focus by numerous organizations. This is a list of these organisations (largely US based)

Call for more transparency on how such tools are used in public services as 20 councils stop using computer algorithms. The Home Office recently stopped using an algorithm to help decide visa applications after allegations that it contained “entrenched racism”.

On the questionable use of Artificial Intelligence for job applications. An exclusive data analysis by BR (Bavarian Broadcasting) data journalists shows that an AI for personality assessment can be swayed by appearances. This might perpetuate stereotypes while potentially costing candidates the job.

Centre for Critical Race and Digital Studies. List of publications, books, reports and videos regarding the intersection of race & digital/data/tech.

Can transparency bring accountability to public-sector algorithmic decision-making (ADM) systems? High-profile retractions have taken place against a shift in public sentiment towards greater scepticism and mistrust of ‘black box’ technologies, evidenced in increasing awareness of the possible risks for citizens of the potentially invasive profiling.

A Home Office-funded project that used artificial intelligence to predict gun and knife crime was found to be wildly inaccurate. “Basing our arguments on inaccuracy is problematic because the tech deficiencies are solvable through time. Even if the algorithm was set to be 100 percent accurate, there would still be bias in this system.”

Machine learning programs trained with patients’ own reports find problems that doctors miss—especially in Black people. Full study here: https://go.nature.com/3pnOwWP

Colonialism in the age of Artificial Intelligence takes the form of “state-of-the-art algorithms” and “AI driven solutions” unsuited to African problems, and hinders the development of local products, leaving the continent dependent on Western software and infrastructure.

As we hand over decision-making regarding social issues to automated systems developed by profit-driven corporates, not only are we allowing our social concerns to be dictated by the profit incentive, but we are also handing over moral and ethical questions to the corporate world, argues ABEBA BIRHANE

This year, in honor of Black History Month, the Codecademy Team is celebrating Black leaders that are working to build a more inclusive, more welcoming, and more diverse tech industry. It's important to celebrate Black people in all our roles and diversity. For UK Black History Month (BHM), we're keen to see similar profiling of technologists who want to raise their visibility, so we can celebrate their work.

Automated decision making threatens to disproportionately impact society’s most vulnerable communities living at the intersection of economic and social marginalization. The report discusses

'Machine learning is revolutionising healthcare provision and delivery, from mobilising previously inaccessible data sources to generating increasingly powerful algorithmic constructs for prognostic modelling. However, it is becoming increasingly obvious that if we do not learn from the mistakes of our past, that we are doomed to repeat them; if it isn’t already too late'

The difficulty to translate reported AI harms into consequences for those responsible

Mapping a child's digital footprint across England's state education landscape. Policy recommendations for building a rights' respecting digital environment

Perhaps without even noticing, you have read about data racism several times in the news in the past months. What is it? This blog seeks to explain - in the context of an emerging strand of work at the European Network Against Racism exploring racism in the digital space.

Maybe more people are writing about the real and potential problems of technology today than ever before. That is mostly a good thing. The…

A growing number of firms are using artificial intelligence to pass or fail jobseekers.

"By embedding race into the basic data and decisions of health care, these algorithms propagate race-based medicine. Many of these race-adjusted algorithms guide decisions in ways that may direct more attention or resources to white patients than to members of racial and ethnic minorities"

“It’s almost an imperative, I think, to drive that diversity,” she said. “Diversity from a gender perspective, but also from other perspectives such as age, race, ethnicity, geography, and many others, because we’re seeing AI is such a powerful technology, and we need to make sure it is equitable.”

Stories from across the world on how to make AI more culturally responsive

Mental health symptoms affect your behaviour online. This could be harnessed to improve detection and treatment