Interesting if you are tracking this stuff.  I can’t see training officers on every tool, but they should have an idea of the capabilities of each. 

https://www.nbcsandiego.com/news/local/san-diego-police-reveal-surveillance-technology-tools/3239207/

San Diego Police Reveals List of What Surveillance Technology Tools it Uses

If you've ever wondered what spy technology the San Diego Police Department uses to help solve crimes, now is your chance to find out.

The department published a list on Thursday of surveillance technologies it already uses or wishes to use this year.

Concern: If you go beyond the legal requirements are you handicapping your AI? 

https://dl.acm.org/doi/abs/10.1145/3593434.3593453 

Implementing AI Ethics: Making Sense of the Ethical Requirements

Society’s increasing dependence on Artificial Intelligence (AI) and AI-enabled systems require a more practical approach from software engineering (SE) executives in middle and higher-level management to improve their involvement in implementing AI ethics by making ethical requirements part of their management practices.  However, research indicates that most work on implementing ethical requirements in SE management primarily focuses on technical development, with scarce findings for middle and higher-level management.  We investigate this by interviewing ten Finnish SE executives in middle and higher-level management to examine how they consider and implement ethical requirements.  We use ethical requirements from the European Union (EU) Trustworthy Ethics guidelines for Trustworthy AI as our reference for ethical requirements and an Agile portfolio management framework to analyze implementation.  Our findings reveal a general consideration of privacy and data governance ethical requirements as legal requirements with no other consideration for ethical requirements identified.  The findings also show practicable consideration of ethical requirements as technical robustness and safety for implementation as risk requirements and societal and environmental well-being for implementation as sustainability requirements.  We examine a practical approach to implementing ethical requirements using the ethical risk requirements stack employing the Agile portfolio management framework.

Clean data, clean answers? 

https://www.intechopen.com/online-first/1121510

Ethics in Scientific Research - New Perspectives [Working Title]

Artificial Intelligence (AI) equips machines with the capacity to learn.  AI frameworks employing machine learning can discern patterns within vast data sets and construct intricate, interconnected systems that yield results that enhance the effectiveness of decision-making processes.  AI, in particular machine learning, has been positioned as an important element in contributing to as well as providing decisions in a multitude of industries.  The use of machine learning in delivering decisions is based on the data that is used to train the machine learning algorithms.  It is imperative that when machine learning applications are being considered that the data being used to train the machine learning algorithms are without bias, and the data is ethically used.  This chapter focuses on the ethical use of data in developing machine learning algorithms.  Specifically, this chapter will include the examination of AI bias and ethical use of AI, data ethics principles, selecting ethical data for AI applications, AI and data governance, and putting ethical AI applications into practice.

Warms the cockles of my auditor’s heart…

https://digitalcommons.law.scu.edu/chtlj/vol39/iss3/1/ 

ALGORITHMIC AUDITING: CHASING AI ACCOUNTABILITY

Calls for audits to expose and mitigate harms related to algorithmic decision systems are proliferating,3 and audit provisions are coming into force—notably in the E.U. Digital Services Act.4  In response to these growing concerns, research organizations working on technology accountability have called for ethics and/or human rights auditing of algorithms and an Artificial Intelligence (AI) audit industry is rapidly developing, signified by the consulting giants KPMG and Deloitte marketing their services.5  Algorithmic audits are a way to increase accountability for social media companies and to improve the governance of AI systems more generally.  They can be elements of industry codes, prerequisites for liability immunity, or new regulatory requirements.6  Even when not expressly prescribed, audits may be predicates for enforcing data-related consumer protection law, or what U.S. Federal Trade Commissioner Rebecca Slaughter calls “algorithmic justice.” 7  The desire for audits reflect a growing sense that algorithms play an important, yet opaque, role in the decisions that shape people’s life chances—as well as a recognition that audits have been uniquely helpful in advancing our understanding of the concrete consequences of algorithms in the wild and in assessing their likely impacts.8

A topic of interest.

https://link.springer.com/article/10.1007/s43681-023-00299-0

Navigating the legal landscape of AI copyright: a comparative analysis of EU, US, and Chinese approaches

This paper compares AI copyright approaches in the EU, US, and China, evaluating their effectiveness and challenges.  It examines the recognition of AI-generated works as copyrightable and the exclusive rights of copyright owners to reproduce, distribute, publicly display, and perform such works.  Differences in approaches, such as recognizing AI as a sui generis right holder in the EU and the broad fair use doctrine in the US, are highlighted.  This paper evaluates strengths and weaknesses of each approach, including enforcement and ownership of copyright in AI-generated works, and clarifies issues related to AI and copyright.  While the EU and US have more developed legal frameworks for AI copyright than China, all three approaches face challenges that need addressing.  This paper concludes by providing insight into the legal landscape of AI copyright and steps necessary for effective protection and use of AI-generated works.

This was not the first impression of educators.  (Panic) 

https://journals.sfu.ca/jalt/index.php/jalt/article/view/797

The use of ChatGPT in the digital era: Perspectives on chatbot implementation

The rapid advancement of technology has led to the integration of ChatGPT, an artificial intelligence (AI)-powered chatbot, in various sectors, including education.  This research aims to explore the perceptions of educators and students on the use of ChatGPT in education during the digital era.  This study adopted a qualitative research approach, using in-depth interviews to gather data.  A purposive sampling technique was used to select ten educators and 15 students from different academic institutions in Krabi, Thailand.  The data collected was analysed using content analysis and NVivo.  The findings revealed that educators and students generally have a positive perception of using ChatGPT in education.  The chatbot was perceived to be a helpful tool for providing immediate feedback, answering questions, and providing support to students.  Educators noted that ChatGPT could reduce their workload by answering routine questions and enabling them to focus on higher-order tasks.  However, the findings also showed some concerns regarding the use of ChatGPT in education.  Participants were worried about the accuracy of information provided by the chatbot and the potential loss of personal interaction with teachers.  The need for privacy and data security was also raised as a significant concern.  The results of this study could help educators and policymakers make informed decisions about using ChatGPT in education.

Tools & Techniques.

https://www.makeuseof.com/the-6-best-ai-tools-for-researchers-and-teachers/

The 6 Best AI Tools for Researchers and Teachers

Artificial intelligence can, when used correctly, offer several benefits for researchers and teachers. Here are some tools to help with your efforts.