糖心TV

Despite warnings about Bias in Computer Systems going back to at least 1992, it has taken concerted
efforts by groups of researchers like FAT/ML (Fairness, Accountability and Transparency in Machine
Learning) and news and media reports highlighting discriminatory effects in data driven algorithms
(e.g. recidivism prediction) to dispel the na茂ve-optimistic myth that the logic/mathematics of
computation would automatically results in objectively unbiased outcomes.
Starting from a brief overview of key challenges in the design and use of data driven algorithmic
decision making systems I will focus in on the inherently socio-technical nature of real-world
applications that give rise to concerns about bias.
I will present some of the findings of the UnBias project on the lived experience and concerns of
teenaged digital natives regarding their interactions with algorithmically mediated services and
discuss the ongoing work of the IEEE P7003 working group for developing a 鈥淪tandard for
Algorithmic Bias Considerations鈥�.
I will conclude with a review of some of the policy making initiatives that were recently launched by
professional associations (e.g. ACM principles; IEEE Global Initiative on Ethical Considerations in AI
and Autonomous Systems), industry led organizations (e.g. Partnership on AI) and regional/national
government bodies (e.g. EC Algorithmic Awareness Building; TransAlgo in France; UK parliamentary
inquiries).