The Right To Confront Your Accusers

Matthews, Jeanna; Babaeianjelodar, Marzieh; Lorenz, Stephen; Matthews, Abigail; Njie, Mariama; Adams, Nathaniel; Krane, Dan E.; Goldthwaite, Jessica; Hughes, Clinton

doi:10.1145/3306618.3314279

Cited by 11 publications

(5 citation statements)

References 8 publications

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“…However, because the authors of IBDGem made their work open and available, we were able to explore some of our results in their software. This level of openness is admirable and not universal in forensic genetics (Chin et al, 2019;Edge & Matthews, 2022;Matthews et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

On forensic likelihood ratios from low-coverage sequencing

Ouerghi,

Krane,

Edge

2024

Preprint

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With advances in sequencing technology, forensic workers can access genetic information from increasingly challenging samples. A recently published computational approach,IBDGem, analyzes sequencing reads, including from low-coverage samples, in order to arrive at likelihood ratios for tests of identity. Here, we show that likelihood ratios produced byIBDGemtest a null hypothesis different from the traditional one used in a forensic genetics context. In particular, rather than testing the hypothesis that the sample comes from a person unrelated to the person of interest,IBDGemtests the hypothesis that the sample comes from an individual who is included in the reference sample used to run the method. This null hypothesis is not generally of forensic interest, because the defense hypothesis is not that the evidence comes from an individual included in a reference panel. Further, it does not take into account genetic variation outside the reference panel, and as a result, the computed likelihood ratios can be much larger than likelihood ratios computed for the standard forensic null hypothesis, often by many orders of magnitude, thus potentially creating an impression of stronger evidence for identity than is warranted. We lay out this result and illustrate it with examples, giving suggestions for directions that might lead to likelihood ratios that have the traditional interpretation.

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Section: Discussionmentioning

confidence: 99%

On forensic likelihood ratios from low-coverage sequencing

Ouerghi,

Krane,

Edge

2024

Preprint

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“…Some of her research concerns the software used in the criminal justice systems. 5,6,7 "In a democracy, we believe in a fair trial. We say 'innocent until proven guilty' and that we have the 'right to confront our accusers.'…”

Section: Criminal Justice Softwarementioning

confidence: 99%

Unfairness Is Everywhere, so What to Do? An Interview With Jeanna Matthews

Johnson,

Menzies

2023

IEEE Softw.

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SE AND ETHICS"UNFAIRNESS IS EVERYWHERE," says Dr. Jeanna Matthews from Clarkson University. "We say we want a fair society, but we are far from a level playing field. If we ask 'what is the secret of your success?', people often think of their own hard work or a good decision they made. However, It is often more accurate to look at advantages, like the ability to borrow money from family and friends when you are in trouble, deep network connections so that you hear about opportunities or have a human look at your application, the ability to move on from a mistake that might send someone else to jail, help at home to care for children, etc.

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“…According to the developers of one PGS system, 80% of certified U.S. labs are currently using the system in casework, are in the process of validating the software for use in casework, or have purchased the software but have not yet started the validation process [3]. Yet, the reliability of PGS systems on the complex DNA samples they are often used to analyze is the subject of much debate [17,61,62,67].…”

Section: Probabilistic Genotyping Software (Pgs)mentioning

confidence: 99%

“…Many agree upon the reliability of traditional methods of DNA analysis for high-quality, single-source DNA samples (e.g., a large amount of minimallydegraded DNA from a single individual), but the reliability of PGS for complex DNA mixtures remains the subject of much debate[17,61,62,67].…”

mentioning

confidence: 99%

(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court

Jin,

Salehi

2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Accountable use of AI systems in high-stakes settings relies on making systems contestable. In this paper we study efforts to contest AI systems in practice by studying how public defenders scrutinize AI in court. We present findings from interviews with 17 people in the U.S. public defense community to understand their perceptions of and experiences scrutinizing computational forensic software (CFS) -automated decision systems that the government uses to convict and incarcerate, such as facial recognition, gunshot detection, and probabilistic genotyping tools. We find that our participants faced challenges assessing and contesting CFS reliability due to difficulties (a) navigating how CFS is developed and used, (b) overcoming judges and jurors' non-critical perceptions of CFS, and (c) gathering CFS expertise. To conclude, we provide recommendations that center the technical, social, and institutional context to better position interventions such as performance evaluations to support contestability in practice.CCS Concepts: • Human-centered computing → Empirical studies in HCI.

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The Right To Confront Your Accusers

Cited by 11 publications

References 8 publications

On forensic likelihood ratios from low-coverage sequencing

On forensic likelihood ratios from low-coverage sequencing

Unfairness Is Everywhere, so What to Do? An Interview With Jeanna Matthews

(Beyond) Reasonable Doubt: Challenges that Public Defenders Face in Scrutinizing AI in Court

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