Blog by the Centre for Data Ethics and Innovation (UK): “To move the recommendation that we made in our review into bias in algorithmic decision-making forward, we have been working with the Central Digital and Data Office (CDDO) and BritainThinks to scope what a transparency obligation could look like in practice, and in particular, which transparency measures would be most effective at increasing public understanding about the use of algorithms in the public sector.
Due to the low levels of awareness about the use of algorithms in the public sector (CDEI polling in July 2020 found that 38% of the public were not aware that algorithmic systems were used to support decisions using personal data), we opted for a deliberative public engagement approach. This involved spending time gradually building up participants’ understanding and knowledge about algorithm use in the public sector and discussing their expectations for transparency, and co-designing solutions together.
For this project, we worked with a diverse range of 36 members of the UK public, spending over five hours engaging with them over a three week period. We focused on three particular use-cases to test a range of emotive responses – policing, parking and recruitment.
The final stage was an in-depth co-design session, where participants worked collaboratively to review and iterate prototypes in order to develop a practical approach to transparency that reflected their expectations and needs for greater openness in the public sector use of algorithms.
What did we find?
Our research validated that there was fairly low awareness or understanding of the use of algorithms in the public sector. Algorithmic transparency in the public sector was not a front-of-mind topic for most participants.
However, once participants were introduced to specific examples of potential public sector algorithms, they felt strongly that transparency information should be made available to the public, both citizens and experts. This included desires for; a description of the algorithm, why an algorithm was being used, contact details for more information, data used, human oversight, potential risks and technicalities of the algorithm…(More)”.