Kathy Baxter, Principal Architect of Ethical AI Practice at Salesforce
The AI and ML deployments are well underway, but for CXOs the biggest issue will be managing these initiatives, and figuring out where the data science team fits in and what algorithms to buy versus build.
Read nowAccording to the research by Kathy Baxter, principal architect for Salesforce's ethical AI practice, it is clear that artificial intelligence (AI) development is a high priority for enterprises, with worldwide AI spending expected to hit $110 billion in 2024. And of executives focused on AI, 80% are struggling to establish processes to ensure responsible AI use. Baxter's research also notes that 93% of consumers say companies have a responsibility to look beyond profit to impact society positively. In addition, 79% of the workforce would consider leaving an employer that demonstrates poor ethics. My conversation with business leaders regarding the future impact of AI often leads to core values, guiding principles and the ethical use of immerging new technologies like machine learning, natural language processing and chatbots, computer visioning, deep learning and smart robotics.
As a Principal Architect of Ethical AI Practice at Salesforce, Baxter develops research-informed best practice to educate Salesforce employees, customers, and the industry on the development of responsible AI. She collaborates and partners with external AI and ethics experts to continuously evolve Salesforce policies, practices, and products. Prior to Salesforce, Baxter worked at Google, eBay, and Oracle in User Experience Research. Baxter is the author of ""="">.
According to Baxter, developing ethical AI is not a nice-to-have but the responsibility of the entire organization. Baxter goes further by noting that responsible AI use is table stakes for businesses. Baxter references research notes that 86% of consumers say they would be more loyal -- and 69% say they would spend more money -- with a company that demonstrates good ethics.
I asked Baxter to share her team's findings on best establishing an ethical AI practice based on their proven methodology at Salesforce. At Salesforce and Tableau, Baxter and her team worked closely with all stakeholders (customers, employees, business partners and our communities) to develop and implement AI responsibly aimed to reduce bias and mitigate risks to the company and customers.
Before we describe the AI maturity model, Baxter and her team believe that core values must be clearly defined and communicated. The core values begin with the notion that the benefits of AI should be accessible to everyone. Here is their research team's commitment statement: "We believe the benefits of AI should be accessible to everyone. But it is not enough to deliver only the technological capabilities of AI -- we also have an important responsibility to ensure that AI is safe and inclusive for all. We take that responsibility seriously and are committed to providing our employees, customers, and partners with the tools they need to develop and use AI safely, accurately, and ethically."
5 core value pillars further define the AI practice commitment:
Trusted AI Principles (from principles to practice)
There are four stages in the AI practice maturity model: ad hoc, organized and repeatable, managed and sustainable and optimized and innovative.
Baxter reminds us that historically, early advocates for this approach have taken on full-time roles within their companies to build an ethical AI practice. The process of having this formal role created and filled can take a year or more of building trust among leaders and demonstrating the importance of developing AI responsibly. However, as more executives see the importance of a responsible AI practice, companies without an internal advocate are now looking to hire from outside.
Entire teams and dedicated budgets do not emerge overnight, so ethics reviews by the lone ethics expert are often ad-hoc and limited to individuals or small teams that have bought into the importance of a responsible AI practice. These small successes are critical in building up a portfolio of "wins" and earning more advocates across the company.
Responsible AI development lifecycle
Ethical AI Practice Maturity Model
Baxter also warns companies about metrics and the important role they play in developingAI-powered products and services. "Product roadmaps and resources should explicitly require that ethical debt is addressed and features to help customers use your AI responsibly are regularly developed. With the prior establishment of metrics, it is now possible to set minimum ethics thresholds for launch in order to block the launch of any new product or feature that does not meet that threshold," said Baxter.
A strong AI ethical practice will include multiple success metrics that are deeply understood and discussed regularly prior to new product launches.
"The Ethical AI field is relatively new, and we are all learning together as we understand risks and harms associated with certain AI technologies or applications of them to different populations. The proposed maturity model will change as our understanding and practice develops, and it is our hope that we can co-create this field together," said Baxter. Baxter and her team regularly post articles about AI ethics that can be found at https://einstein.ai/ethics.
This article was co-authored by Kathy Baxter. As a Principal Architect of Ethical AI Practice at Salesforce, Kathy develops research-informed best practice to educate Salesforce employees, customers, and the industry on the development of responsible AI. She collaborates and partners with external AI and ethics experts to continuously evolve Salesforce policies, practices, and products. Prior to Salesforce, she worked at Google, eBay, and Oracle in User Experience Research. She received her MS in Engineering Psychology and BS in Applied Psychology from the Georgia Institute of Technology. The second edition of her book, "Understanding your users," was published in May 2015. You can read about her current research at einstein.ai/ethics.