Remove Data Science Remove Explainable AI Remove Responsible AI
article thumbnail

Navigating AI Bias: A Guide for Responsible Development

Unite.AI

If AI systems produce biased outcomes, companies may face legal consequences, even if they don't fully understand how the algorithms work. It cant be overstated that the inability to explain AI decisions can also erode customer trust and regulatory confidence. Visualizing AI decision-making helps build trust with stakeholders.

Algorithm 162
article thumbnail

3 key reasons why your organization needs Responsible AI

IBM Journey to AI blog

Gartner predicts that the market for artificial intelligence (AI) software will reach almost $134.8 Achieving Responsible AI As building and scaling AI models for your organization becomes more business critical, achieving Responsible AI (RAI) should be considered a highly relevant topic. billion by 2025.

professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The ODSC East 2025 Schedule: 150+ AI & Data Science Sessions, Keynotes, & More

ODSC - Open Data Science

From May 13th to 15th, ODSC East 2025 is bringing together the brightest minds in AI and data science for an unparalleled learning and networking experience. With 150+ expert-led sessions, hands-on workshops, and cutting-edge talks, youll gain the skills and insights needed to stay ahead in the rapidly evolving AI landscape.

article thumbnail

Bring light to the black box

IBM Journey to AI blog

Challenges around managing risk and reputation Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it. Responsible AI use is critical, especially as more and more organizations share concerns about potential damage to their brand when implementing AI.

Metadata 227
article thumbnail

Enhancing AI Transparency and Trust with Composite AI

Unite.AI

Composite AI plays a pivotal role in enhancing interpretability and transparency. Combining diverse AI techniques enables human-like decision-making. Key benefits include: reducing the necessity of large data science teams. Explainability also aligns with business ethics and regulatory compliance.

article thumbnail

The Essential Tools for ML Evaluation and Responsible AI

ODSC - Open Data Science

As AI systems become increasingly embedded in critical decision-making processes and in domains that are governed by a web of complex regulatory requirements, the need for responsible AI practices has never been more urgent. But let’s first take a look at some of the tools for ML evaluation that are popular for responsible AI.

article thumbnail

Top ODSC East 2023 Virtual Sessions Available to Watch for Free

ODSC - Open Data Science

Interactive Explainable AI Meg Kurdziolek, PhD | Staff UX Researcher | Intrinsic.ai Although current explainable AI techniques have made significant progress toward enabling end-users to understand the why behind a prediction, to effectively build trust with an AI system we need to take the next step and make XAI tools interactive.