Blog
Purpose
1. to provide the legal and business communities a curated series of articles …
… to keep pace with the rapid and sweeping societal changes initiated by the rise of AI while mitigating the risks of government overreach or excessive litigation.
2. to provide a private forum for members of The Sedona Conference Working Group 13 on AI Law to engage in an ongoing dialogue…
… advancing its efforts to draft consensus, nonpartisan commentaries, including Principles and Best Practice recommendations to move the law forward “in a reasoned and just way.”
*Note: Nothing in this blog constitutes legal advice or the formation of any attorney-client relationship. See Disclaimers.
Sponsors
Dasein Capital
AIVN
The Sedona Conference
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What Getting Fired Taught Me About Due Process—A Better Vision For AI-Assisted Due Process For All [Part 3 of 3]
Discover how role-segregated AI agents can revolutionize due process without replacing human judgment. Machines organize complexity; humans deliver justice.
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What Getting Fired Taught Me About Due Process—Why AI Can’t Deliver Justice… [Part 2 of 3]
AI was supposed to eliminate human bias in decision-making—but in reality, it threatens to automate injustice at scale. Learn why AI-driven adjudication risks eroding fairness, amplifying bias, and contaminating the very idea of due process.
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Mayday for May Day?
Participated in both the May Day protest march and the Law Day renewal of attorney oaths ceremony in downtown Phoenix yesterday. Two very different groups, but same foundation. “Where the law ends, tyranny begins.” The Constitution matters. Faith in our prospects for justice and the rule of law restored.
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“Delete All IP law”? Why the Tech Titans Want to Pull Up the Ladder Behind Them
Elon Musk and Jack Dorsey want to “delete all IP.” But in an AI-driven future, reform—not repeal—of IP law is essential for creators and startups.
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Data Is the New Oil: How to License Datasets for AI Without Losing Control (Part 2 of 2)
In Part 1 of this article, we explained the differences between local vs. cloud licensing models and why cloud access is ideal from the licensor’s perspective but often unrealistic due to standard industry practices. We ended by highlighting the need for data licensors to mitigate the security risks that come with providing local copies of…
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Data Is the New Oil: How to License Datasets for AI Without Losing Control (Part 1 of 2)
Learn the risks of dataset licensing for AI, why cloud-only access is ideal, and how to protect your data against leakage, misuse, and exfiltration.
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What Getting Fired Taught Me About Due Process—To Which AI Might Giveth and Taketh Away… [Part 1 of 3]
Fired midseason with no hearing, a high school coach reflects on what due process really means in schools today—and why every institution must uphold it, especially as AI begins making high-stakes decisions.
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“Because I’m not white and I’m a girl…”: On the AI road to autonomous discrimination? [Part 2 of 2]
As AI becomes increasingly embedded in decision-making, its potential to reinforce systemic discrimination is both undeniable and alarming. My daughter’s experience—facing bias as a girl playing boys’ varsity basketball—mirrors the broader reality of how unchecked human prejudices can be codified into algorithmic decision-making. While AI can theoretically serve as a force for fairness, it is…
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“Because I’m not white and I’m a girl…”: On the AI road to autonomous discrimination? [Part 1 of 2]
In a world where systemic discrimination still lingers, my daughter’s experience as both an aspiring lawyer and a standout athlete on a boys’ varsity team highlights the persistent biases that shape opportunity. Whether on the courtroom floor or the basketball court, she has faced skepticism, not due to her abilities, but because of entrenched perceptions…
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In LLM Providers We Trust…? [Conclusion of the Parsing the Blame for AI series]
AI disrupts traditional liability frameworks, making strict product liability ill-suited and negligence hard to establish. Data privacy laws are outdated and ineffective against AI’s pervasive data collection, while algorithmic discrimination in hiring and services introduces bias risks. Regulatory actions, like FTC oversight, remain limited, leaving significant gaps in accountability and relying heavily on future legislative…