Artificial intelligence brings vast opportunities to next-generation businesses, but great power comes with great responsibility.
AI has the capacity to directly impact billions of lives…positively and negatively. When AI is used to make hiring decisions, companies may benefit from an expedited or streamlined application process –– but qualified individuals may not receive a career-altering job offer due to inadvertent bias from an AI algorithm, limiting professional development and talent growth. On a societal level, industries like finance, transportation, and healthcare rely on accurate insights from data to provide essential services to the population. However, algorithmic bias threatens the efficacy of these industry decisions with severe implications: Some communities may not receive essential services because an algorithm determines that providing services and other communities is a more profitable business move. Take, for example, the widening social inequities in healthcare that stem from biases in data and algorithms.
Growing socioeconomic inequality, job automation, privacy risks, and even weapons automation are known risks of irresponsible AI. These potential negative impacts around AI have (reasonably) raised considerable questions about trusting the technology, its ethical use, and its legal implications. In a recent report from Accenture, only 35% of global consumers said they trust how companies are using AI. 77% of respondents – an overwhelming majority – stated organizations should be held responsible for misusing AI.
Given the state of public sentiment, companies looking to adopt and increase their use of AI must take steps to use the technology in a responsible manner through an emerging area of business management called AI Trust, Risk, and Security Management (AI TRiSM).
See The Importance of AI TRiSM for more on the impact of responsibly implementing and using AI.
What is AI TRiSM and Responsible AI?
AI TRiSM involves a multifaceted strategy aimed at addressing the ethical, business, and legal concerns around AI.
While regulations around the world are slowly evolving to address concerns around AI, the creation of dependable standards is currently performed at the discretion of an organization’s data scientists and software developers. Organizations have a strong incentive to create dependable standards and there is a growing awareness around this issue. In a recent survey of risk managers, 58 percent of respondents said AI currently has the greatest potential for unintended consequences. Just 11 percent of surveyed risk managers said their organization is fully capable of analyzing the risks connected to AI adoption.
One emerging governance framework designed to document how an organization addresses the legal and ethical challenges surrounding AI is called Responsible AI. This framework and the surrounding initiatives are focused on resolving ambiguity in an effort to prevent negative unintended consequences.
The tenants of a Responsible AI framework address the designing, developing, and use of AI in ways that empower employees, provide value to customers and fairly impact society. A successful Responsible AI framework allows companies to build trust around AI and scale up the technology in a responsible way.
The core principles of Responsible AI framework deal with
- Ethical use
Well-defined testing and governance metrics must be designed to protect algorithms from malicious actors.
Users of AI should be able to describe the purpose for using it, the rationale for adopting it, and any associated decision-making process in a way that is easily understood by end users.
Measures should be developed to identify and eliminate any systemic bias.
AI systems should be able to operate on a continual basis and react quickly to environmental changes.
While legal and ethical concerns are the primary drivers behind the adoption of Responsible AI, the use of this governance framework has also been shown to provide business benefits. According to research, companies that have put the proper governance frameworks in place, including Responsible AI, have seen almost three times the return on investments in AI compared to companies that have not put these frameworks in place.
The Difficulties of Implementing Responsible AI in an Organization
Organizations looking to implement Responsible AI must focus on translating ethical principles into quantifiable metrics that can be used in everyday operations. This calls for making:
- Reputational considerations
Technical Difficulties Of Implementing AI TRiSM
The effectiveness of a Responsible AI framework can’t be measured using tried and true business metrics like website traffic or click-through rates. New technical metrics must be developed to monitor factors related to AI trust, AI risk, and AI security. Without good metrics and methods, organizations will find it difficult to effectively maintain their Responsible AI framework. They will also find it difficult to perform essential decision-making and build consensus around AI initiatives. There are promising signs, however, that counterfactual analyses and metrics like error rates making it easier for organizations to implement a Responsible AI framework.
Organizational Difficulties of Implementing AI TRiSM
Because the principles of Responsible AI come out of ethical concerns, it is critical for companies leveraging AI to have an organizational culture that encourages its people to raise concerns regarding AI systems. Too often, fears of decreased innovation or productivity prevent people from coming forward, and this can have a negative impact on risk mitigation. Along with establishing solid metrics related to Responsible AI, organizations need to provide training and incentives that empower employees to make the right decisions.
Operational Difficulties of Implementing AI TRiSM
Organizations using AI should have governance structures in place to address accountability, conflict resolution, and competing incentives. These structures should be transparent and focused on addressing any misalignment, bureaucratic issues, and lack of clarity regarding AI-related operations.
Reputational Difficulties of Implementing AI TRiSM
Ongoing and proactive approaches to Responsible AI can help prevent an organization from suffering reputational damage as a result of AI. This involves healthy skepticism from internal stakeholders, as ethical principles can shift due to changing opinions or recent events. Ongoing and well-intentioned scrutiny encourages the regular pressure testing of an organization’s Responsible AI framework.
How TripleBlind Supports Responsible AI
AI systems are built on vast amounts of data, and protecting that data is critical to addressing the trust and security risks of AI.
Innovative technology from TripleBlind is specifically designed to secure data for the development of AI technology. Specifically, our Blind Learning data tools allow AI developers to access vast amounts of sensitive data without ever exposing their proprietary algorithms. We believe in:
- Protecting the model by distributing learning, never revealing an entire model to any data provider
- Reducing the burden on data providers by unlocking more data partners, solving for communication overhead and easing collaboration through our simple-to-use API.
- Dividing and conquering model training, optimizing for computational resources among partner organizations
- Blind Decorrelation, guarding against membership inference attacks and preventing actors from predicting or uncovering training data
Are you ready to remove common barriers to using high-quality and ethical data for AI? We help solve for data access, prep, and bias challenges. Train new models on remote data and run inference on existing models –– all while protecting the privacy, fidelity, and intellectual property value of your data. Contact us today to learn more, or download our Whitepaper to deep-dive into our technology.