Optimization is widely used for decision making across various domains, valued for its ability to improve efficiency. However, poor implementation practices can lead to unintended consequences, particularly in socioeconomic contexts where externalities (costs or benefits to third parties outside the optimization process) are significant. To propose solutions, it is crucial to first characterize involved stakeholders, their goals, and the types of subpar practices causing unforeseen outcomes. This task is complex because affected stakeholders often fall outside the direct focus of optimization processes. Also, incorporating these externalities into optimization requires going beyond traditional economic frameworks, which often focus on describing externalities but fail to address their normative implications or interconnected nature, and feedback loops. This paper suggests a framework that combines systems thinking with the economic concept of externalities to tackle these challenges. This approach aims to characterize what went wrong, who was affected, and how (or where) to include them in the optimization process. Economic externalities, along with their established quantification methods, assist in identifying "who was affected and how" through stakeholder characterization. Meanwhile, systems thinking (an analytical approach to comprehending relationships in complex systems) provides a holistic, normative perspective. Systems thinking contributes to an understanding of interconnections among externalities, feedback loops, and determining "when" to incorporate them in the optimization. Together, these approaches create a comprehensive framework for addressing optimization's unintended consequences, balancing descriptive accuracy with normative objectives. Using this, we examine three common types of subpar practices: ignorance, error, and prioritization of short-term goals.

Algorithmic decision-making systems sometimes produce errors or skewed predictions toward a particular group, leading to unfair results. Debiasing practices, applied at different stages of the development of such systems, occasionally introduce new forms of unfairness or exacerbate existing inequalities. We focus on post-processing techniques that modify algorithmic predictions to achieve fairness in classification tasks, examining the unintended consequences of these interventions. To address this challenge, we develop a set of measures that quantify the disparity in the flips applied to the solution in the post-processing stage. The proposed measures will help practitioners: (1) assess the proportionality of the debiasing strategy used, (2) have transparency to explain the effects of the strategy in each group, and (3) based on those results, analyze the possibility of the use of some other approaches for bias mitigation or to solve the problem. We introduce a methodology for applying the proposed metrics during the post-processing stage and illustrate its practical application through an example. This example demonstrates how analyzing the proportionality of the debiasing strategy complements traditional fairness metrics, providing a deeper perspective to ensure fairer outcomes across all groups.

Despite all the industry hype and genuine advances, generative AI models are still prone to odd, inexplicable, and downright worrisome quirks. According to recent evidence, the industry's newer reasoning models may already possess the ability to manipulate and circumvent their human programmers' goals. Some AI will even attempt to cheat their way out of losing in games of chess. This poor sportsmanship is documented in a preprint study from Palisade Research, an organization focused on risk assessments of emerging AI systems. While supercomputers--most famously IBM's Deep Blue--have long surpassed the world's best human chess players, generative AI still lags behind due to their underlying programming parameters.

Parents spoke after the Antioch High School shooting on Wednesday, Jan. 22, outside of Nashville, Tennessee. Antioch High School in Nashville, Tennessee, where a deadly shooting took place last Wednesday, did not have metal detectors due to some administrators' concerns about racism, the New York Post reported. "I knew this day was gonna happen," Fran Bush, a former Metro Nashville Public Schools (MNPS) board member, told the New York Post. "I knew it was gonna happen just because it's like a free open door, everybody coming in." The shooting, which left 16-year-old student Josselin Corea Escalante and the suspect dead, has parents calling for the school to bring in metal detectors after the AI security system failed to detect the 17-year-old gunman's weapon.

Building a successful AI copilot requires a systematic approach. This paper is divided into two sections, covering the design and evaluation of a copilot respectively. A case study of developing copilot templates for the retail domain by Microsoft is used to illustrate the role and importance of each aspect. The first section explores the key technical components of a copilot's architecture, including the LLM, plugins for knowledge retrieval and actions, orchestration, system prompts, and responsible AI guardrails. The second section discusses testing and evaluation as a principled way to promote desired outcomes and manage unintended consequences when using AI in a business context. We discuss how to measure and improve its quality and safety, through the lens of an end-to-end human-AI decision loop framework. By providing insights into the anatomy of a copilot and the critical aspects of testing and evaluation, this paper provides concrete evidence of how good design and evaluation practices are essential for building effective, human-centered AI assistants.

ChatGPT, a software seeking to simulate human conversational abilities, is attracting increasing attention. It is sometimes portrayed as a groundbreaking productivity aid, including for creative work. In this paper, we run an experiment to assess its potential in complex writing tasks. We ask the software to compose a policy brief for the Board of the Bank of Italy. We find that ChatGPT can accelerate workflows by providing well-structured content suggestions, and by producing extensive, linguistically correct text in a matter of seconds. It does, however, require a significant amount of expert supervision, which partially offsets productivity gains. If the app is used naively, output can be incorrect, superficial, or irrelevant. Superficiality is an especially problematic limitation in the context of policy advice intended for high-level audiences.

Discount regularization, using a shorter planning horizon when calculating the optimal policy, is a popular choice to restrict planning to a less complex set of policies when estimating an MDP from sparse or noisy data (Jiang et al., 2015). It is commonly understood that discount regularization functions by de-emphasizing or ignoring delayed effects. In this paper, we reveal an alternate view of discount regularization that exposes unintended consequences. We demonstrate that planning under a lower discount factor produces an identical optimal policy to planning using any prior on the transition matrix that has the same distribution for all states and actions. In fact, it functions like a prior with stronger regularization on state-action pairs with more transition data. This leads to poor performance when the transition matrix is estimated from data sets with uneven amounts of data across state-action pairs. Our equivalence theorem leads to an explicit formula to set regularization parameters locally for individual state-action pairs rather than globally. We demonstrate the failures of discount regularization and how we remedy them using our state-action-specific method across simple empirical examples as well as a medical cancer simulator.

The last year has been characterized by a rush of new artificial intelligence (AI) programs being released into the world since OpenAI, a lab backed by Microsoft, launched ChatGPT in November 2022. Both Microsoft and Google rolled out products in March that they say will use AI to transform work, and IBM's CEO Arvind Krishna said the company's AI tool will be able to reduce 30 to 50% of repetitive office work. Since taking the helm at Microsoft in 2014, at a time when its market dominance with traditional software offerings was waning, Satya Nadella has focused on ensuring the company remains relevant. . The company has invested heavily in Azure, its cloud computing platform, and in AI, pouring at least $13 billion in the leading lab OpenAI. Microsoft's share price has risen nearly tenfold since Nadella became CEO, outperforming the S&P 500, which has merely doubled its value over the same time.

AI technologies like ChatGPT are necessitating a fundamental overhaul of our educational systems and institutions. Getting the right answers to predetermined tests is no longer sufficient in an age where AI can access, integrate, and recite knowledge billions if not trillions of times faster than the human mind. So, what are the skills, capabilities, and experiences that our students and citizens will need to prosper in an age where personal and professional success will be based on the application, not the memorization and regurgitation, of knowledge? Let's continue that conversation here in Part II to define the requirements for humans to excel in creating organizational and societal value in a world dominated by AI and Big Data. Many organizations engage in a "wear'em down" decision-making process when dealing with wicked hard challenges with multiple opposing views.

At the recent QCon London conference, Mathew Lodge, CEO of DiffBlue, gave a presentation on the advancements in artificial intelligence (AI) for writing code. Lodge highlighted the differences between Large Language Models and Reinforcement Learning approaches, emphasizing what both approaches can and can't do. The session gave an overview of the state of the current state of AI-powered code generation and its future trajectory. In his presentation, Lodge delved into the differences between AI-powered code generation tools and unit test writing tools. Code generation tools like GitHub Copilot, TabNine, and ChatGPT primarily focus on completing code snippets or suggesting code based on the context provided.