Sep 27, 2026 · 40 min · 12 segments
**Episode 5: In Discussion with Kalliopi Terzidou – From Courtrooms to Chatbots: Can AI Close the Justice Gap?** As generative AI continues to reshape the legal landscape, fundamental questions arise…
Kalliopi TerzidouGuest
Frédérique BoulangerHostOne aspect that stood out to me in your work is the way you highlight that understanding AI tools is fundamentally a democratic issue.
Indeed, without proper comprehension and critical engagement from legal professionals, these tools risk becoming, if I may borrow Cathy O'Neill's phrase, weapons of mass destruction.
Is that how you approach AI tools in your research, weapons that we should use carefully?

Of course, artificial intelligence does not differ from other types of technologies.

A judge that consults judge EPT on how to decide on a case and then uses the system's recommendation as her decision is actively compromising her independence.

She's no longer the one making the decision, but has delegated her unjudicative power to a machine.

On the contrary, the generation of a summary of a given case does not hold as many risks for the independence and impartiality of the judge.

Therefore, the way we use the system determines whether it is a weapon or not.

I have to say that in the case of AI systems, it also depends on how we train them.

A well-known example of problematic training is the COMPAS system, which is a risk assessment tool used in US courts to predict the likelihood that a defendant will re-offend.

The COMPAS system, C-O-M-P-A-S, for your audience, was found to be biased against because it disproportionately labeled black defendants as having a higher risk of reoffending compared to white defendants with similar profiles.

This bias arose from the data used to train the algorithm, which reflected historical racial disparities in arrests and sentencing.

As a result, the system perpetuated existing inequalities rather than offering an objective assessment.

So training is really important when we want to use responsibly AI systems in our daily workflows.
One aspect that stood out to me in your work is the way you highlight that understanding AI tools is fundamentally a democratic issue.
Indeed, without proper comprehension and critical engagement from legal professionals, these tools risk becoming, if I may borrow Cathy O'Neill's phrase, weapons of mass destruction.
Is that how you approach AI tools in your research, weapons that we should use carefully?

Of course, artificial intelligence does not differ from other types of technologies.

A judge that consults judge EPT on how to decide on a case and then uses the system's recommendation as her decision is actively compromising her independence.

She's no longer the one making the decision, but has delegated her unjudicative power to a machine.

On the contrary, the generation of a summary of a given case does not hold as many risks for the independence and impartiality of the judge.

Therefore, the way we use the system determines whether it is a weapon or not.

I have to say that in the case of AI systems, it also depends on how we train them.

A well-known example of problematic training is the COMPAS system, which is a risk assessment tool used in US courts to predict the likelihood that a defendant will re-offend.

The COMPAS system, C-O-M-P-A-S, for your audience, was found to be biased against because it disproportionately labeled black defendants as having a higher risk of reoffending compared to white defendants with similar profiles.

This bias arose from the data used to train the algorithm, which reflected historical racial disparities in arrests and sentencing.

As a result, the system perpetuated existing inequalities rather than offering an objective assessment.

So training is really important when we want to use responsibly AI systems in our daily workflows.
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