|Talks|

Unaccountable Algorithms: From "Move Fast and Break Things" to "Beyond a Reasonable Doubt"

Visiting speaker
In-person
Past Talk
Will Landecker
Chief Scientist, Tech Integrity Lab
Fri
,
Oct 23, 2026
10:30 am
EST
Oct 23, 2026
10:30 am
In-person
Portsoken Street
London, E1 8PH, UK
The Roux Institute
Room
100 Fore Street
Portland, ME 04101
Network Science Institute
2nd floor
Network Science Institute
11th floor
177 Huntington Ave
Boston, MA 02115
Network Science Institute
2nd floor
Rm 134, 4th Floor, 101 BV
Room
58 St Katharine's Way
London E1W 1LP, UK
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Talk recording

The phrase "it's in the data" has become synonymous with "it's objectively true." But datasets, and algorithms derived from them, are curated, designed, and built artifacts. They reflect the inevitable compromises that must be made during the design process. As our ability to collect data and build powerful algorithms grows, so does our ability to be misled by subtle and unintentional problems of construct validity, alignment, and evaluation. Will Landecker has worked three sides of this issue: a PhD in machine learning interpretability, a decade leading data science and machine-learning teams in Silicon Valley, and now an expert poring over algorithmic evidence in criminal cases. In this talk, he will share stories from experiences in academia, industry, and the justice system. We will see examples where those different sectors keep each other in balance, and others where they amplify each other's problems.

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About the speaker
Will Landecker is the chief scientist of the Tech Integrity Lab (https://www.ti-lab.org/) and the owner of Accountable Algorithm (https://www.accountablealgorithm.com/), a technical consulting company for responsible AI. He has been a data scientist, research scientist, machine learning engineer, and technical lead at tech companies like Twitter, Lyft, Stripe, and Nextdoor, where he built and evaluated algorithms for accuracy, safety, transparency, and bias. His work includes the measurement and remediation of misbehaving or misaligned algorithmic systems through both technical and policy decisions. He is a member of the IEEE Working Group on the Standard for the Procurement, Verification and Validation, and Life Cycle Management of Forensic Technologies, as well as the IEEE AI Policy Committee. He holds a PhD in computer science (dissertation advised by Melanie Mitchell) from Portland State University, and an undergraduate degree in mathematics from Reed College.
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Oct 23, 2026