Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation

• URL: http://arxiv.org/abs/2405.09679v2
• Date: Fri, 26 Jul 2024 21:23:14 GMT
• Title: Simulating Policy Impacts: Developing a Generative Scenario Writing Method to Evaluate the Perceived Effects of Regulation
• Authors: Julia Barnett, Kimon Kieslich, Nicholas Diakopoulos,
• Abstract summary: We use GPT-4 to generate scenarios both pre- and post-introduction of policy. We then run a user study to evaluate these scenarios across four risk-assessment dimensions. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security.
• Score: 3.2566808526538873
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rapid advancement of AI technologies yields numerous future impacts on individuals and society. Policymakers are tasked to react quickly and establish policies that mitigate those impacts. However, anticipating the effectiveness of policies is a difficult task, as some impacts might only be observable in the future and respective policies might not be applicable to the future development of AI. In this work we develop a method for using large language models (LLMs) to evaluate the efficacy of a given piece of policy at mitigating specified negative impacts. We do so by using GPT-4 to generate scenarios both pre- and post-introduction of policy and translating these vivid stories into metrics based on human perceptions of impacts. We leverage an already established taxonomy of impacts of generative AI in the media environment to generate a set of scenario pairs both mitigated and non-mitigated by the transparency policy in Article 50 of the EU AI Act. We then run a user study (n=234) to evaluate these scenarios across four risk-assessment dimensions: severity, plausibility, magnitude, and specificity to vulnerable populations. We find that this transparency legislation is perceived to be effective at mitigating harms in areas such as labor and well-being, but largely ineffective in areas such as social cohesion and security. Through this case study we demonstrate the efficacy of our method as a tool to iterate on the effectiveness of policy for mitigating various negative impacts. We expect this method to be useful to researchers or other stakeholders who want to brainstorm the potential utility of different pieces of policy or other mitigation strategies.

Related papers

• Towards Leveraging News Media to Support Impact Assessment of AI Technologies [3.2566808526538873]
  Expert-driven frameworks for impact assessments (IAs) may inadvertently overlook the effects of AI technologies on the public's social behavior, policy, and the cultural and geographical contexts shaping the perception of AI and the impacts around its use. This research explores the potentials of fine-tuning LLMs on negative impacts of AI reported in a diverse sample of articles from 266 news domains spanning 30 countries around the world to incorporate more diversity into IAs.
  arXiv  Detail & Related papers  (2024-11-04T19:12:27Z)
• Using Scenario-Writing for Identifying and Mitigating Impacts of Generative AI [3.2566808526538873]
  Impact assessments have emerged as a common way to identify the negative and positive implications of AI deployment. But it is also essential to critically interrogate the current literature and practice on impact assessment. In this provocation, we do just that by first critiquing the current impact assessment literature and then proposing a novel approach.
  arXiv  Detail & Related papers  (2024-10-31T07:48:58Z)
• IntOPE: Off-Policy Evaluation in the Presence of Interference [23.167697252901398]
  Off-Policy Evaluation (OPE) is employed to assess the potential impact of a hypothetical policy. IntIPW is an IPW-style estimator that integrates marginalized importance weights to account for both individual actions and the influence of adjacent entities.
  arXiv  Detail & Related papers  (2024-08-24T06:07:25Z)
• Reduced-Rank Multi-objective Policy Learning and Optimization [57.978477569678844]
  In practice, causal researchers do not have a single outcome in mind a priori. In government-assisted social benefit programs, policymakers collect many outcomes to understand the multidimensional nature of poverty. We present a data-driven dimensionality-reduction methodology for multiple outcomes in the context of optimal policy learning.
  arXiv  Detail & Related papers  (2024-04-29T08:16:30Z)
• Game and Reference: Policy Combination Synthesis for Epidemic Prevention and Control [4.635793210136456]
  We present a novel Policy Combination Synthesis (PCS) model for epidemic policy-making. To prevent extreme decisions, we introduce adversarial learning between the model-made policies and the real policies. We also employ contrastive learning to let the model draw on experience from the best historical policies under similar scenarios.
  arXiv  Detail & Related papers  (2024-03-16T00:26:59Z)
• Anticipating Impacts: Using Large-Scale Scenario Writing to Explore Diverse Implications of Generative AI in the News Environment [3.660182910533372]
  We aim to broaden the perspective and capture the expectations of three stakeholder groups about the potential negative impacts of generative AI. We apply scenario writing and use participatory foresight to delve into cognitively diverse imaginations of the future. We conclude by discussing the usefulness of scenario-writing and participatory foresight as a toolbox for generative AI impact assessment.
  arXiv  Detail & Related papers  (2023-10-10T06:59:27Z)
• Conformal Off-Policy Evaluation in Markov Decision Processes [53.786439742572995]
  Reinforcement Learning aims at identifying and evaluating efficient control policies from data. Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty.
  arXiv  Detail & Related papers  (2023-04-05T16:45:11Z)
• Policy Dispersion in Non-Markovian Environment [53.05904889617441]
  This paper tries to learn the diverse policies from the history of state-action pairs under a non-Markovian environment. We first adopt a transformer-based method to learn policy embeddings. Then, we stack the policy embeddings to construct a dispersion matrix to induce a set of diverse policies.
  arXiv  Detail & Related papers  (2023-02-28T11:58:39Z)
• CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies [62.39667564455059]
  We consider and study a distribution of optimal policies. In experimental simulations we show that CAMEO indeed obtains policies that all solve classic control problems. We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability.
  arXiv  Detail & Related papers  (2022-05-19T09:48:56Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• Efficient Evaluation of Natural Stochastic Policies in Offline Reinforcement Learning [80.42316902296832]
  We study the efficient off-policy evaluation of natural policies, which are defined in terms of deviations from the behavior policy. This is a departure from the literature on off-policy evaluation where most work consider the evaluation of explicitly specified policies.
  arXiv  Detail & Related papers  (2020-06-06T15:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.