This article was written by Aakritti Srikanth and originally appeared on the DataRobot Blog here: https://www.datarobot.com/blog/trusted-ai-and-detecting-bias-with-mlops-governance/
In an environment of increasing scrutiny, the need to deliver trusted AI has never been greater. However, many organizations don’t implement any feedback loop to monitor and control their models after they’re out in production. It is becoming increasingly important to mitigate algorithmic bias to prevent discrimination in use cases such as facial recognition and loan lending. For instance, the New York Times recently reported, “We Need Laws to Take On Racism and Sexism in Hiring.”
Across the globe, governments and enterprises have put in place regulatory requirements, standards, certifications, and audits to oversee machine learning and AI applications, especially with autonomous decision-making systems (e.g., General Data Protection Regulation, California Consumer Privacy Act, SR 11-7 Guidance on Model Risk Management, etc).
With the increase in stricter government regulations, there could likely be compulsory auditing of all machine learning models in critical applications. AI governance and auditability will affect all sectors that utilize machine learning.
A growing number of businesses in the financial sector, such as banks, insurance, and payment card companies, may be liable for hefty fines and penalties due to non-compliance with standard industry regulations. Problems prevail because of the absence of acceptable practices when it comes to proper regulatory guidance in the domain of bias mitigation and the incorrect implementation of tools. A few examples include:
- JPMorgan settled their mortgage discrimination suite for $55M.
- In October 2020, Citigroup was slammed with a $400 million fine for ‘longstanding failure’ to resolve its risk and data systems.
The EU recently proposed regulations that would punish companies offering racially biased technology. If an international law is passed that requires auditing of machine learning models, all institutions using machine learning in critical applications will be looking for a solution.
DataRobot MLOps solution is ideal in this journey of fairness and trustworthy AI.
MLOps governance is a comprehensive AI audit solution for machine learning testing and governance. It empowers enterprises to measure, monitor, and manage AI-introduced risks at scale. C-level buyers in compliance, audit, and risk use the AI governance solution to reduce regulatory risks.
An organization’s present or past behavior is assessed for consistency with relevant principles, regulations, or norms through the auditing process. The AI governance solution enables internal or external stakeholders to conduct an official testing and inspection of their machine learning model to gain visibility into the quality of the models and to enable their compliance with enterprise or external regulations. Machine learning auditing and governance can promote consistency of testing and accountability across the organization. The current methods used by audit firms are costly and time-consuming. Challenges exist for businesses leveraging machine learning, especially for use cases such as risk scoring, fraud detection, and underwriting in financial services organizations.
DataRobot MLOps has strong governance capabilities to help stakeholders understand how models arrive at their outcomes, assess them for efficacy and risks, and recommend actions that should be taken to mitigate bias.
Its technology enables:
- Trustworthy AI: Adaptable end-to-end trustworthy AI governance platform that can create a centralized, collaborative testing and audit system that is understandable.
- AI Explainability: MLOps can be leveraged by both business and technical stakeholders, address the needs of different stakeholders, and enable cross-functional workflows.
- Comprehensive AI: Enable business stakeholders who understand the regulatory compliance, financial, and brand risks but might not have AI expertise, such as Chief Risk and Governance Officers. Chief AI Officers and data scientists of their teams can also utilize it to improve AI explainability and establish trustworthy AI.
- Model Fairness: Mitigation of algorithmic bias to prevent discrimination in use cases such as facial recognition and fraud detection (risk assessment).
- Model Monitoring: Automate monitoring of models in production with alerts set up for detection of any anomalies and drift in data/performance.
At DataRobot, we believe that trust is essential. We provide the expertise and the tools to test your systems across multiple dimensions of trust to design AI that performs exceptionally.