As global discourse on AI regulation gains momentum, this paper focuses on delineating the impact of ML on autonomy and fostering awareness. Respect for autonomy is a basic principle in bioethics that establishes persons as decision-makers. While the concept of autonomy in the context of ML appears in several European normative publications, it remains a theoretical concept that has yet to be widely accepted in ML practice. Our contribution is to bridge the theoretical and practical gap by encouraging the practical application of autonomy in decision-making within ML practice by identifying the conditioning factors that currently prevent it. Consequently, we focus on the different stages of the ML pipeline to identify the potential effects on ML end-users' autonomy. To improve its practical utility, we propose a related question for each detected impact, offering guidance for identifying possible focus points to respect ML end-users autonomy in decision-making.

IBM reveals that nearly half of the challenges related to AI adoption focus on data complexity (24%) and difficulty integrating and scaling projects (24%). While it may be expedient for marketers to "slap a GPT suffix on it and call it AI," businesses striving to truly implement and incorporate AI and ML face a two-headed challenge: first, it's difficult and expensive, and second, because it's difficult and expensive, it's hard to come by the "sandboxes" that are necessary to enable experimentation and prove "green shoots" of value that would warrant further investment. In short, AI and ML are inaccessible. History shows that most business shifts at first seem difficult and expensive. However, spending time and resources on these efforts has paid off for the innovators.

Abstract--Continuous Integration (CI) has become a wellestablished software development practice for automatically and continuously integrating code changes during software development. An increasing number of Machine Learning (ML) based approaches for automation of CI phases are being reported in the literature. It is timely and relevant to provide a Systemization of Knowledge (SoK) of ML-based approaches for CI phases. Our systematic analysis also highlights the deficiencies of the existing ML-based solutions that can be improved for advancing the state-of-the-art. Given the variety of employed techniques in applying ML solutions in CI, and growing interest in this domain, it is In recent years, the software development industry has seen necessary to systematically identify state-of-the-art practices a significant shift towards the adoption of Continuous Integration used for automating CI tasks through ML methods.

The volume of money laundering and other financial crimes is growing worldwide--and the techniques used to evade their detection are becoming ever more sophisticated. This has elicited a vigorous response from banks, which, collectively, are investing billions each year to improve their defenses against financial crime (in 2020, institutions spent an estimated $214 billion on financial-crime compliance). 1 1. What's more, the resulting regulatory fines related to compliance are surging year over year as regulator's impose tougher penalties. But banks' traditional rule- and scenario-based approaches to fighting financial crimes has always seemed a step behind the bad guys, making the fight against money laundering an ongoing challenge for compliance, monitoring, and risk organizations. Now, there is an opportunity for banks to get out in front.

GEICO is more than insurance, it's truly a tech company at heart. GEICO's Technology Solutions is rapidly expanding to keep up with its growth in the digital space. GEICO Technology Solutions is seeking a Machine Learning Engineer. The Machine Learning Engineer is a highly motivated technical leader in machine learning space to drive ML strategy and architecture for GEICO, and will play a critical role shaping the ML landscape that helps GEICO transforms into a data driven company. Machine Learning Engineer has deep expertise in both ML architecture and ML engineering disciplines to ensure that strategy and tactics align.

By Esther Rietmann, Director of Content and Programming at Data Science Salon Enterprises leverage machine learning (ML) to improve business operations, adapt to changing business requirements, and gain insights into market trends. A 2021 ML market study indicates that 59% of all large enterprises are deploying ML solutions. ML techniques can enable fast and accurate decision-making to avoid costly corrective measures and ensure solid business reputation. We at Data Science Salon spoke with some of the experts who will be speaking at Data Science Salon Miami Hybrid on September 21 to expand the understanding of ML in the enterprise, its key challenges and trends. Read this post to gain insightful answers that will guide you in the AI adoption journey!

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that are often used interchangeably. While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Rather, ML is a major subset of AI.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! Within the last decade, the terms artificial intelligence (AI) and machine learning (ML) have become buzzwords that are often used interchangeably. While AI and ML are inextricably linked and share similar characteristics, they are not the same thing. Rather, ML is a major subset of AI.

Security-constrained unit commitment (SCUC) is solved for power system day-ahead generation scheduling, which is a large-scale mixed-integer linear programming problem and is very computationally intensive. Model reduction of SCUC may bring significant time savings. In this work, a novel approach is proposed to effectively utilize machine learning (ML) to reduce the problem size of SCUC. An ML model using logistic regression (LR) algorithm is proposed and trained with historical nodal demand profiles and the respective commitment schedules. The ML outputs are processed and analyzed to reduce variables and constraints in SCUC. The proposed approach is validated on several standard test systems including IEEE 24-bus system, IEEE 73-bus system, IEEE 118-bus system, synthetic South Carolina 500-bus system and Polish 2383-bus system. Simulation results demonstrate that the use of the prediction from the proposed LR model in SCUC model reduction can substantially reduce the computing time while maintaining solution quality.

Building a machine learning model involves a lot of steps - these steps are not limited to objective guidelines and require a more elaborative approach and depth based on the complexity of the business problem. The business problem can be solved in multiple ways - you need to decide whether the machine learning solution is really needed or it can be solved with a simple heuristic? Is there already a solution that is currently serving the business problem? If so, you need to do a thorough analysis and understand its limitations and seek the machine learning solution that can best overcome them. The next step should be to compare the two solutions - does the proposed machine learning solution also come with its limitations.