The Data Strategy Show

A handful of genuinely useful frames emerge - governance as a post-harm mop-up exercise, the CDP analogy for AI disclosure, and the small-vendor agentic risk - but long stretches are consumed by the host's rambling editorialising and mutually agreeable observations that never deepen into tactics or mechanisms.

“what we're doing is we're treating governance as a uh, mop up exercise. The harm is already being caused by the time the remediation comes into place”

“CDP Carbon Disclosure Project was a third sector organization. It started sort of early 2000s to gain some traction because it was demanding that companies disclose their carbon emissions”

The reframe of women's slower AI adoption as rational skepticism rather than a capability deficit is genuinely counterintuitive, and the CDP-to-AI governance analogy is a fresh structural argument; however most surrounding content (bias in training data, hallucinations, facial recognition bias) recycles standard AI-ethics talking points.

“maybe that slower adoption, maybe that more cynical approach, maybe that more reticent approach is actually pretty rational and pretty wise given what we do and what we don't know about how AI works”

“surely your strongest skeptic is the person that you really want to win over”

Louise brings a legitimately cross-disciplinary background - financial services litigation, third-sector C-suite, philosophy - that gives her AI-ethics commentary real texture, but she is primarily a commentator and writer rather than someone who has deployed or governed AI systems at operational scale inside a business.

“I started off, I did a philosophy degree and then went into law as a financial services litig for an international law firm, um, during the credit crisis”

“then I did a fairly sort of dramatic pivot into international development and C suite leadership within the third sector”

The facial recognition stat (35% higher misidentification for Black women) is a concrete, citable data point, and the CDP origin story adds historical specificity, but the remainder of the episode relies on vague qualifiers ('lots of noise', 'a few weeks ago', 'lots and lots') and the host actively introduces factual errors (citing GPT's launch as 'November 20, 2002') that go unchallenged.

“Black women are 35% more likely to be wrongly identified by facial recognition software than a white man”

“CDP Carbon Disclosure Project was a third sector organization. It started sort of early 2000s”

The host's questions are consistently multi-part, self-interrupting, and laden with his own opinions and anecdotes, effectively answering the question before the guest can; there is no meaningful pushback, no follow-up on specific claims, and a factual error about GPT's launch date goes completely uncorrected.

“And is that happening more? Is that happening more here? Do you see that that's now becoming a greater risk, um, for people? Um, you talked about ethnicity there and you talked about the fact that, you know, it's actually having a detrimental effect to women, um, you know, and specifically black women”

“I think, I think you're right. I think it's getting better. Because if you go back to 2002, I remember speaking to a uh, researcher um, in the area”