Foundational ethical principles. The truth about AI bias / Cassie Kozyrkov -- Introducing ethicize, the fully AI-driven cloud-based ethics solution! / Brian T. O'Neill -- "Ethical" is not a binary concept / Tim Wilson -- Cautionary ethics tales : phrenology, eugenics, ... and data science? / Sherrill Hayes -- Leadership for the future : how to approach ethical transparency / Rado Kotorov -- Rules and rationality / Christof Wolf Brenner -- Bill Schmarzo -- Be careful with "decisions of the heart" / Hugh Watson -- Fairness in the age of algorithms -- Data science ethics : what is the foundational standard? / Mario Vela -- Understand who your leaders serve / Hassen Masum -- Data science and society. Unbiased [is not] fair : for data science, it cannot be just about the math / Doug Hague -- Trust, data science, and Stephen Covey / James Taylor -- Ethics must be a cornerstone of the data science curriculum / Linda Burtch -- Data storytelling : the tipping point between fact and fiction / Brent Dykes -- Informed consent and data literacy education are crucial to ethics / Sherrill Hayes -- First, do no harm / Eric Schmidt -- Why research should be reproducible / Stuart Buck -- Build multiperspective AI / Hassan Masum and Sébastien Paquet -- Ethics as a competitive advantage / Dave Mathias -- Algorithmic bias : are you a bystander or an upstander? / Jitendra Mudhol and Heidi Livingston Eisips -- Data science and deliberative justice : the ethics of the voice of "the other" / Robert J. McGrath -- Spam. Are you going to miss it? / John Thuma -- Is it wrong to be right? / Marty Ellingsworth -- We're not yet ready for a trustmark for technology / Hannah Kitcher and Laura James -- The ethics of data. How to ask for customers' data with transparency and trust / Rasmus Wegener -- Data ethics and the lemming effect / Bob Gladden -- Perceptions of personal data / Irina Raicu -- Should data have rights? / Jennifer Lewis Priestley
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