Overview

We deliver trustworthy AI solutions

Many enterprises are hindered in making full use of AI, usually due to a lack of trust in the solutions. Our IBM Expert Labs team works across all stages of the AI lifecycle, from plan to build, deploy to operate, to help deliver trustworthy AI solutions at scale and speed.

Aspects of trustworthy AI

Evaluating your AI solutions

Fair

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Ensure models and outcomes are impartial and avoid bias. Fairness avoids privileged groups having a systemic advantage over others.

Private

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Follow high-integrity data and business compliance guidelines. Privacy assures owners that they retain control of data and insights.

Explainable

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Provide explanations to understand outcomes and decisions. Users should be able to understand why AI arrived at a conclusion and at which point it would have been different.

Transparent

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Inspire with trust and transparency for inspection. Transparent results mean that users can increase their understanding of why and how AI was created.

Robust

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Handle exceptional conditions effectively and with ease. Robust AI should be able to evaluate and defend itself against a variety of threats.

Benefits

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Innovate with confidence

Create solutions where trustworthiness is built in from the start.
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Quickly move to market

Integrate trust, avoid policy delays, and go rapidly from idea to deployment in a matter of weeks.
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Deploy trustworthy operational AI

Go beyond data science experiments to AI in production that continuously earns trust.
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Increase ROI with validated AI

Generate real business value with AI that you know applies to the data you are seeing in real time.
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Maximize opportunity with confidence

Learn from best practices in order to consistently create AI that you can trust for the best outcomes.

Three pillars to ensure trustworthy AI success

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People

Stakeholders, data scientists, and designers working together to define and implement what trust means for a given solution.

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Platform

Data governance, model monitoring, and model governance all working together, providing an environment of trust.

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Process

Best practices to integrate trust concerns at every stage of the life cycle, so common pitfalls can be avoided.

AI risks: three real world examples

Fairness

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Fairness

Fifty men and fifty women apply for a job. Forty men are selected, but only twenty-five women. The company must understand how the model reached this conclusion and evaluate if it is biased or not.

Explainability

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Explainability

A customer is rejected for a loan, while his neighbor is accepted for the exact same loan, and with excellent terms. Can the business provide an explanation to the customer?

Drift

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Drift

What was fraudulent activity last month, may less likely be fraudulent activity this month. How do we know the ML/DL models are still accurate and don’t provide bad business decisions?

Solutions

Education

Provide training and enablement

Benefit from IBM’s expertise on trust in AI, including best practices and industry-driven recommendations. Provide training and enablement on all aspects of the AI lifecycle. “Learn through doing” with side-by-side work in planning, building, deploying, and operating trustworthy AI solutions.

Solution planning for AI

Create an AI action plan

In planning AI solutions, it is critical to translate business needs into specific, actionable requirements to ensure trust in the solution itself, as well as its monitoring and maintenance. Solution planning for AI uses a structured method to establish AI business needs and translate them into precise technical specifications.

AI build

Create trustworthy AI solutions with an agile approach

At the core of any business’s use of AI is a specific AI solution that must be trusted, usually a machine learning model. An experienced team of data scientists and AI practitioners produces an initial solution with the characteristics needed for trust in just six weeks using agile methodologies.

MLOps validate and deploy

Efficient and reliable AI deployment

Even the best AI model  brings no value to the business until it can be confidently deployed and used.  The key to promoting models from development to test in production is validation-- not only of the accuracy, but also of trustworthy characteristics and configuration management, which must be maintained in order to trust what is promoted. MLOps Validate and Deploy establishes pipelines for the full process, regardless of what tools were used to build models.

MLOps monitor and manage

Operating with trust and transparency

Even with the best processes for planning and building a trustworthy solution, we need special monitoring and processes for machine learning models to be able to use them confidently. MLOps monitor and manage uses IBM Cloud Pak® for Data and OpenScale™ to establish operational monitoring for key elements of trustworthy AI.

Speak with an expert

Have questions? Schedule a free 30-minute consultation.

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