In the premiere episode of Tech Film Noir, hosts Lianne Potter, Jeff Watkins, and Simon Painter travel back to 1984 to dissect James Cameron’s career-defining masterpiece, The Terminator.

*** Regular Compromising Positions Resume on 30th April!***

We’re putting Arnold’s cyborg under the microscope - literally. From the 6502 assembly language hidden in the Terminator’s HUD to the ‘Right to Repair’ scene that would make a modern technician weep, we explore why this low-budget slasher-turned-sci-fi remains the gold standard for AI storytelling. We also tackle the tough questions: Why does time travel require nudity (and will it encourage us to be ‘beach ready’ in the future)? And can we please acknowledge that Kyle Reese saved humanity while wearing deeply questionable, possibly biohazard-level trousers?

Whether you're here for the technical deep dive into Agentic AI or the high-octane roast of Terminator: Genisys, this episode has enough 80s nostalgia to power a Walkman for a decade.

Stick around to the end for our completely serious (not serious) food and drink pairings.

When movies guess the future, we check their work.

Subscribe here:

Youtube: https://www.youtube.com/@TechFilmNoir

Spotify: https://open.spotify.com/show/7tb4fGTPsLO8ZxOeJMZV8U?si=2ddc9cd5153a44bc

Apple Podcasts: https://podcasts.apple.com/gb/podcast/tech-film-noir-a-technology-and-film-podcast/id1892857131

What happens when the welfare state designs its technology to side-eye first and ask questions later?

In this episode, we take a ride into the world of self-driving cars and ask: What happens to trust when your car gets hacked?

Drawing upon a 2025 autonomous car-hacking experiment, we explore how trust is built, broken, and crucially, whether that trust can be repaired once a system puts you in harms way.

This isn’t just about cars. It’s about what happens when we hand over control to a system we don’t fully understand.

Expect human factors, socio-technical theory, real-world cyber scenarios, and the uncomfortable reality that fixing the system isn’t the same as fixing trust.

In This Episode, We Discuss:

The Attack Surface is Trust: Why the real vulnerability in autonomous systems isn’t the code, it’s human belief.

Hack vs Bug: Why a malicious attack hits differently than a system error (and why that distinction matters).

Transparency After a Breach: Does telling people the truth about a cyber attack actually rebuild trust or just make them more nervous?

The Social Truth about Trust: Why you’re not just trusting the car, but the company, the regulators and the entire system behind it.

LINKS

The Impact of Cybersecurity Attacks on Human Trust in Autonomous Vehicle Operations by Cherin Lim, David Predez, Linda Ng Boyle and Prashanth Rajivan (2025)

Foundations for an Empirically Determined Scale of Trust in Automated Systems by Jiun-Yin Jian, Ann Bisantz, Colin Drury, and James Llinas (1998)

Test your morals with the Moral Machine game.

What happens when the welfare state designs its technology to side-eye first and ask questions later?

In this episode of Compromising Positions, we get hands-on with Big Brother Watch’s “Suspicion by Design” report, unpacking how the UK Department for Work and Pensions (DWP) uses algorithmic profiling and AI systems to detect Universal Credit fraud and why defaulting to suspicion is a dangerous position for any government to take.

This episode is a measured examination of welfare AI, algorithmic decision-making, and what happens to trust, consent, and dignity when systems are built to watch first and explain never.

Expect socio-technical theory, legal realities, real-world harms, and the kind of uncomfortable questions policymakers really don’t like being asked.

In This Episode, We Discuss:

Suspicion Architecture: What happens when suspicion is a design choice.

The Algorithmic Gaze meets Dataveillance: What happens when you can’t opt out of AI lead services that are inherently bias against you.

Why “Security Through Obscurity” Fails: We show why secrecy doesn’t equal safety.

Fraud Detection that Punishes the Many, not the Few: How to design AI systems that protect public funds without criminalising the people who need it most.

Show Notes

Suspicion by Design: What we know about the DWP’s algorithmic black box, and what it tries to hide by Big Brother Watch (2025)

Surveillance as Social Sorting: Privacy, Risk and Digital Discrimination by David Lyon (Ed) (2003)

Information Technology and Dataveillance by Roger Clarke (1988; 3015)