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Record W3159604639

Courts in the Digital Age: ‘Adaptive Leadership’ for Harnessing Technology and Enhancing Access to Justice

2020· article· en· W3159604639 on OpenAlexaffabout
Karen Eltis

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSupreme courtLawPoliticsEconomic JusticeCharterPolitical scienceHumanityInfallibilitySociology
DOInot available

Abstract

fetched live from OpenAlex

“Never let a good crisis go to waste” - Winston Churchill In an early Star Trek Episode titled “Court Martial” (1967), Captain Kirk stands falsely accused of killing a man, who in the end is revealed to have faked his own demise. A computer recording unequivocally points towards guilt. As Kirk stands trial for criminal negligence and all are convinced of the futuristic machine’s infallibility, the Captain’s eccentric lawyer delivers a stirring speech, questioning the recording and demanding that the human court reconvene “in the name of Humanity fading in the shadow of the machine…lest it elevate the computer above humanity.” Prescient in its questioning and framing of many of the central technology-related socio-political and legal issues of the day, the episode begged the question of technology’s appropriate role in the justice system, generally, and algorithmic enhanced decision making more specifically. Indeed, technology’s perceived infallibility, which paradoxically might give rise to creeping judicial and public complacency in the face of indiscernible bias and an increased cultural reticence to challenge technology (all while resisting professional expertise), could pose serious challenges to the fairness of process and to entrenched Constitutional rights. Conversely, as the Supreme Court of Canada recognized in an era preceding COVID-19, digitization holds tremendous promise for the justice system. Notably, it holds great potential to increase access to and confidence in the justice system, as guaranteed by the Charter. These two concepts are intimately related in that diminution of access leads to diminution of confidence, and the imperatives of access to justice are ill-satisfied when courts are by far overburdened. Undoubtedly, widespread and meaningful access to the justice system is paramount to democracy and the Rule of Law. A fortiori, in times of sanitary urgency such as a pandemic and its socio-economic ramifications and aftermaths of frayed nerves and relationships . While the advantages of recruiting technology to facilitate meaningful and timely access to justice far precedes the pandemic, the current crisis presents an unprecedented opportunity to adapt swiftly yet mindfully to a changed and changing world towards shoring up the Rule of Law . Plainly put, there is no turning back: Only moving forward. As restrictions on social gatherings and movement more generally persist beyond a clearly defined time frame, overstretching courts worldwide – mercilessly be saddling them with more onerous constraints in the circumstances courts are scrambling to go ‘online’ as an interim solution. The prospect of harnessing the promise of AI in the justice context is perhaps more intriguing and indeed compelling now than ever. The objective before courts and policy makers, which this paper speaks to, is to harness technology in a manner that enhances these pillars of the justice system sustainably. In order not to paradoxically undermine confidence, this must be done with an eye towards the long term, contextually – mindfully adapting rather than merely automating, in Susskind's parlance. Ultimately, courts must devise a clear, principled framework that might inspire a more coherent, systematic approach; not only to remote hearings or online courts but framing a role for AI decision-making that passes constitutional muster. Accordingly, this modest first attempt encapsulates two primary guiding principles: First, technology – including but not limited to AI-assisted decision-making can empower courts and litigants including self-representatives and other vulnerable groups in the pandemic and beyond. Thus, Courts can and should be harnessing digitization purposefully to serve access. Second, and towards this very end, we must frame the deployment of technology in a way that mitigates its adverse and unintended effects (rather than resorting to it ad hoc or playing catch up). Indeed, justice is best served with technology employed as an aide; not a crutch deferred to unquestioningly. Plainly put, it is not helpful for courts to suddenly embrace technology as a panacea without nuance or normative support. As Dame Hazel Genn has articulated, “the concept of agile development is the opposite of the unfortunate big bang approaches that we have seen in the past” In other words, Justice does not have the luxury ‘to break things’ (paraphrasing Facebook), even when we move fast! Rather, Justice must take fundamental precautions towards optimizing access substantively, building trust by palliating unintended side-effects. Accordingly, in transitioning to online or virtual courts, triaging cases is of the essence. Technology lends itself better to some cases than others. Online courts or remote courts in their various expressions are not a panacea. A one size fits all prototype would best cede to a hybrid model, which accounts for the specifics of cases both in substance and in terms of the needs of participants. What follows therefore is an overview of three primary challenges arising from the use of new technologies (largely defined) in the Justice setting. The article highlights the most prominent of unintended consequences deriving from the ad hoc use of new technologies. It proceeds with a comparative discussion of recent AI deployment in the US (Loomis) and its broader, more immediate significance for courts and concludes with the relevance of Kahneman’s paradigm Fast/ Think Slow to tech-assisted decision-making.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.016
Scholarly communication0.0140.010
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.119
GPT teacher head0.396
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2020
Admission routes2
Has abstractyes

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