Most businesses expect that artificial intelligence (AI) will save money and promote efficiency as it continues to be adopted and implemented across various industries. But what these companies may not expect – and AI cannot predict – are the myriad ways that AI will result in new and increasing potential for liability. From addiction-forming or defamatory chatbots to automated hiring tools with discriminatory impact, AI-driven technologies […]
Mind the Gap: The Many Faces of ChatGPT in the Mirror
One of the most significant areas of data ethics concern is bias in generative AI models. Concerns about algorithmic bias are not new. Associative model (i.e., neural network) bias is not new. Even the ceding of consequential decisions to these models is not new. Just a few examples: AI is used to screen resumes and evaluate […]
Future-Proofing AI Under a Federal Umbrella: What a 10-Year State Regulation Freeze Means
The federal government’s proposal to impose a 10-year freeze on state-level AI regulation isn’t happening in a vacuum but in direct response to California. The state’s AI Accountability Act (SB 1047) has been making waves for its ambition to hold developers of powerful AI models accountable through mandatory safety testing, public disclosures, and the creation of a new regulatory […]
Ask a Data Ethicist: How Do Technical Data Choices in ML Lead to Ethical Issues?
A lot of times, ethical issues in AI systems arise from the most mundane types of decisions made about data such as how it is processed and prepared for machine learning (ML) projects. I’ve been reading Designing Machine Learning Systems by Chip Huyen, which is filled with practical advice about design choices in machine learning […]
Mind the Gap: AI-Driven Data and Analytics Disruption
We are at the threshold of the most significant changes in information management, data governance, and analytics since the inventions of the relational database and SQL. Most advances over the past 30 years have been the result of Moore’s Law: faster processing, denser storage, and greater bandwidth. At the core, though, little has changed. The basic […]
Why and How to Unlock Proprietary Data to Drive AI Success
These days, virtually every company is using AI – and in most cases, they’re using it through off-the-shelf AI technologies, like Copilot, that offer the same capabilities to every customer. This begs the question: How can a business actually stand out in the age of AI? Rather than just adopting AI as a way of keeping […]
Protecting Machine Learning Systems in the GenAI Era
As GenAI and machine learning (ML) become more widespread across industries, their high levels of adoption have created a major challenge: security. While every organization and IT team has its own security protocols and frameworks, many are starting to realize that traditional approaches aren’t enough when it comes to protecting themselves from the potential threats […]
Why Your Chatbot Is Underperforming – And the Must-Have Capabilities to Turn It Around
More and more organizations are adopting generative AI (GenAI)-powered chatbots that have the power to streamline operations by automating repetitive tasks to improve efficiency and lead to faster, data-driven decisions. The capabilities of these chatbots to achieve greater accuracy are continually expanding, raising the standard for them to deliver more precise outputs that effectively harness […]
Why One-Size-Fits-All Data Governance Doesn’t Work in the Age of AI
Data has evolved from a back-office function into a central driver of innovation, customer experience, and regulatory compliance. Yet, many organizations still apply a one-size-fits-all approach to data governance frameworks, using the same rules for every department, use case, and dataset. Here’s the issue: What works for managing a legacy HR system doesn’t work for […]
DGIQ + AIGov Conference: Takeaways and Cross-Cutting Topics
In this series of blog posts, I aim to share some key takeaways from the DGIQ + AIGov Conference 2024 held by DATAVERSITY. These takeaways include my overall professional impressions and a high-level review of the most prominent topics discussed in the conference’s core subject areas: data governance, data quality, and AI governance. In the first three […]