AI Red Flags
Software engineers often do not take ethical considerations into account in their daily work. This can be due to a lack of resources or knowledge and awareness. We would like to close this gap and offer a workshop which points out important red flags for software developers in order to avoid faults in their Machine Learning systems and thus avert discriminatory effects. The workshop focuses on discrimination and its causes, using real-life examples and interactive worksheets.
Through participation in the workshop, the participants
- … learn what algorithms and Machine Learning are and how they function,
- … get to know sources of error in algorithmic systems, which can be a cause of discrimination,
- … get to know the peculiarities of algorithmically-mediated discrimination,
- … understand how an algorithmic decision-making system can have discriminatory effects, using examples from the worksheets,
- … classify sources of error and special features of algorithmicallymediated discrimination, using examples from the worksheets,
- … learn which red flags to avoid in order to mediate or avoid discriminatory effects.
Partner:
Cathrine is passionate about helping organizations understand the ethical risks of AI and ensuring that algorithms perform as intended without biases from data noise. She believes in using creative innovation and values-based social design to mitigate the social issues of technical systems. She has research-based expertise on AI ethics and provides workshops and consultancy services through her own company, Bui Consulting. She has helped DNB Bank, the biggest financial institution in the Nordics, develop algorithmic impact assessments and prepare for the upcoming EU AI Act regulations. She is a professional speaker with 6 years of experience, holding talks in different places ranging from an ASEAN Human Rights forum, conferences in Mozambique and South Korea, to company events at Cisco. Cathrine is a member of NORDE, Norwegian Council for Digital Ethics, and on the board of Protocols of AI Security IEEE; a global initiative to set up a new research center to improve AI safety.
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