Imagine a world where an advanced and trusted AI system possesses the power to catch criminals as soon as they commit a crime or a country breaks an international agreement. We see a glimpse of this in the form of satellite imagery analysis, which gives people the ability to track the different changes that occur to the earth’s surface. This ability allows different actors (good, neutral, and bad) to have increased surveillance opportunities, which introduces the concept of Responsible AI.
As an AI ethicist specializing in Responsible AI, I understand how essential it is for society to contemplate the consequences of developing such highly advanced technology and ensuring society develops AI responsibly. As Stephen Hawking once said, "Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last unless we learn how to avoid the risks."
Specific to banking, Responsible AI has a major role to play in eliminating biases and hallucinations while enabling transparency. The future of AI in banking, credit unions and beyond depends on the actions of AI engineers, policy-makers, and similar leaders to ensure a safe, responsible coexistence with AI. It all starts with understanding the principles and values associated with Responsible AI.
Responsible AI encompasses the approach of developing AI in a manner that is accountable, ethical, fair, trustworthy, and transparent. The objective is to harness the benefits of AI for society while minimizing potential risks and harm. There are three buckets of fundamental principles associated with Responsible AI: ethics, explainability, and governance, each with associated values underneath, as shown in the figure below.
This branch of Responsible AI centers on the development and use of AI technologies, considering their implications, risks, and impacts. Some example topics that interest AI Ethicists are privacy, bias and discrimination, and job displacement.
Some common values associated AI Ethics include:
Making AI explainable means understanding how the model was designed and how it makes decisions. An example of algorithmic transparency is explaining how a patient's diagnosis was determined in a healthcare setting.
Some common values associated with Explainability include:
Governance refers to the framework, policies, and practices in place to supervise the development and use of AI. An example of this is President Biden’s Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence.
Some common values associated with Governance include:
Posh places great importance on Responsible AI, evident through our guiding principles: "See something, do something; Trust by default; Follow the data; Serve the user; Ensure a quality foundation; Tolerate ambiguity; and Invest in each other." These principles guide our company in advocating for the FinTech community. A prime example is our Privacy efforts, which invokes Serve the User, the practice of ensuring we are making for better customer experiences. We employ a privacy-by-design approach to ensure that we use alternative identifiers (for example changing a PIN number to ****), preventing the identification of individuals as data subjects (a phone number to a Chat Log ID).
Another illustration of the implementation of Responsible AI at Posh is evident in our approach to empowering Client Contact Center Agents, which relies on our principle to Follow the Data. Our objective is to leverage conversational AI as a supportive tool to mitigate common stressors experienced by agents. This involves addressing a range of tasks, including responding to frequently asked questions, facilitating discussions on banking transactions, and enhancing overall knowledge of the contact center operations. We are committed to positioning ourselves as collaborative partners and a tool in this endeavor, aiming to provide assistance rather than merely serving as a mechanism for job automation and elimination.
As more groups actively work towards AI development, it has become imperative for companies to assume the role of creating Responsible AI. Major players, such as Apple, Accenture, Google, and Microsoft have taken significant steps in this direction by creating their own Responsible AI Guidelines. Posh, like other purpose-built AI companies, is looking to major enterprises like these to guide decisions around Responsible AI guidelines while also engaging with financial industry leaders within current technology platforms, the banking sectors, and regulators. With all these different insights and viewpoints in mind, Posh is thoughtfully and carefully building products that are risk-averse and maintain Responsible AI approaches.
If you are on the path to becoming an advocate for Responsible AI, the following resources can assist you in your journey:
Author Kylie Leonard is a Conversational AI Designer at Posh. She holds a Doctorate in Technology degree with a focus in Responsible AI from Purdue University. In her spare time, she enjoys her family and friends, watching reality tv, reading SciFi and fantasy, and taking photos of her cat, Jewel.