Fostering trust in AI systems is a great remaining obstacle to bringing the most transformative AI technologies into reality, such as autonomous vehicles or the large-scale integration of machine intelligence in medicine.
The challenge is to translate guiding ethical principles and aspirations into implementation and make the responsible practice of AI accessible, reproducible, and achievable for all who engage with the design and use of AI systems.
Download now to learn about:
- Practical data quality, model accuracy, robustness, stability, and velocity dimensions of trusted AI
- Compliance, security, humility, and governance considerations, vital for operational trust in AI
- Transparency, bias and fairness, and privacy implications for AI systems striving to deliver unparalleled levels of trust aligned with your organisational values
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Lateral moves in tech: Why leaders should support employee mobilityIn-depth Encouraging staff to switch roles can have long-term benefits for skills in the tech sector
