Clinical AI model reporting cards should be expanded to incorporate a broad
bias reporting of both social and non-social factors. Non-social factors
consider the role of other factors, such as disease dependent, anatomic, or
instrument factors on AI model bias, which are essential to ensure safe
deployment.

临床 AI 模型报告应扩展，包括广泛报道社会和非社会因素的偏差。非社会因素考虑了其他因素的作用，如疾病相关因素、解剖学因素或仪器因素对 AI 模型偏差的影响，这对于确保安全部署是至关重要的。

基准偏见：扩展临床 AI 模型卡以纳入社会和非社会因素的偏见报告

Benchmarking bias: Expanding clinical AI model card to incorporate bias  reporting of social and non-social factors

Counterfactual learning to rank (CLTR) relies on exposure-based inverse
propensity scoring (IPS), a LTR-specific adaptation of IPS to correct for
position bias. While IPS can provide unbiased and consistent estimates, it
often suffers from high variance. Especially when little click data is
available, this variance can cause CLTR to learn sub-optimal ranking behavior.
Consequently, existing CLTR methods bring significant risks with them, as
naively deploying their models can result in very negative user experiences. We
introduce a novel risk-aware CLTR method with theoretical guarantees for safe
deployment. We apply a novel exposure-based concept of risk regularization to
IPS estimation for LTR. Our risk regularization penalizes the mismatch between
the ranking behavior of a learned model and a given safe model. Thereby, it
ensures that learned ranking models stay close to a trusted model, when there
is high uncertainty in IPS estimation, which greatly reduces the risks during
deployment. Our experimental results demonstrate the efficacy of our proposed
method, which is effective at avoiding initial periods of bad performance when
little data is available, while also maintaining high performance at
convergence. For the CLTR field, our novel exposure-based risk minimization
method enables practitioners to adopt CLTR methods in a safer manner that
mitigates many of the risks attached to previous methods.

提出了一种新的风险感知的 CLTR 方法，使用了风险正则化来保证排名模型相对于安全模型的一致性，从而大大降低了模型部署的风险。经实验证明，该方法可以有效避免在数据量较少时出现性能下降的情况，并在收敛后保持高性能，为 CLTR 领域提供了更安全的模型部署方法。

基于曝光的风险最小化的反事实学习排序的安全部署

Safe Deployment for Counterfactual Learning to Rank with Exposure-Based  Risk Minimization

Structured access is an emerging paradigm for the safe deployment of
artificial intelligence (AI). Instead of openly disseminating AI systems,
developers facilitate controlled, arm's length interactions with their AI
systems. The aim is to prevent dangerous AI capabilities from being widely
accessible, whilst preserving access to AI capabilities that can be used
safely. The developer must both restrict how the AI system can be used, and
prevent the user from circumventing these restrictions through modification or
reverse engineering of the AI system. Structured access is most effective when
implemented through cloud-based AI services, rather than disseminating AI
software that runs locally on users' hardware. Cloud-based interfaces provide
the AI developer greater scope for controlling how the AI system is used, and
for protecting against unauthorized modifications to the system's design. This
chapter expands the discussion of "publication norms" in the AI community,
which to date has focused on the question of how the informational content of
AI research projects should be disseminated (e.g., code and models). Although
this is an important question, there are limits to what can be achieved through
the control of information flows. Structured access views AI software not only
as information that can be shared but also as a tool with which users can have
arm's length interactions. There are early examples of structured access being
practiced by AI developers, but there is much room for further development,
both in the functionality of cloud-based interfaces and in the wider
institutional framework.

通过云服务实现结构化访问 AI 系统是避免危险性 AI 能力广泛可用，同时又保留可安全使用的 AI 能力的有效方式，并且要将 AI 软件视为用户可控的工具而非单纯的信息，对此需要对云服务提供更好的功能和广泛框架。