In 2018, Rick Smith, founder and CEO of Axon, the Scottsdale, Arizona-based manufacturer of Taser weapons and body cameras, became concerned that advances in technology were creating new and challenging ethical issues. So, he set up an independent AI ethics board made up of ethicists, AI experts, public policy specialists, and representatives of law enforcement to provide recommendations to Axon’s management. In 2019, the board recommended against adding facial recognition technology to the company’s line of body cameras, and in 2020, it provided guidelines regarding the use of automated license plate recognition technology. Axon’s management followed both recommendations.
In 2022, the board recommended against a management proposal to produce a drone-mounted Taser designed to address mass shootings. After initially accepting the board’s recommendation, the company changed its mind and, in June 2022, in the wake of the Uvalde school shootings, announced it was launching the taser drone program anyway. The board’s response was dramatic: Nine of the 13 members resigned, and they released a letter that outlined their concerns. In response, the company announced a freeze on the project.
As societal expectations grow for the responsible use of digital technologies, firms that promote better practices will have a distinct advantage. According to a 2022 study, 58% of consumers, 60% of employees, and 64% of investors make key decisions based on their beliefs and values. Strengthening your organization’s digital responsibility can drive value creation, and brands regarded as more responsible will enjoy higher levels of stakeholder trust and loyalty. These businesses will sell more products and services, find it easier to recruit staff, and enjoy fruitful relationships with shareholders.
However, many organizations struggle to balance the legitimate but competing stakeholder interests. Key tensions arise between business objectives and responsible digital practices. For example, data localization requirements often contradict with the efficiency ambitions of globally distributed value chains. Ethical and responsible checks and balances that need to be introduced during AI/algorithm development tend to slow down development speed, which can be a problem when time-to-market is of utmost importance. Better data and analytics may enhance service personalization, but at the cost of customer privacy. Risks related to transparency and discrimination issues may dissuade organizations from using algorithms that could help drive cost reductions.
If managed effectively, digital responsibility can protect organizations from threats and open them up to new opportunities. Drawing from our ongoing research into digital transformations and in-depth studies of 12 large European firms across the consumer goods, financial services, information and communication technology, and pharmaceutical sectors who are active in digital responsibility, we derived four best practices to maximize business value and minimize resistance.
1. Anchor digital responsibility within your organizational values.
Digital responsibility commitments can be formulated into a charter that outlines key principles and benchmarks that your organization will adhere to. Start with a basic question: How do you define your digital responsibility objectives? The answer can often be found in your organization’s values, which is articulated in your mission statement or CSR commitments.
According to Jakob Woessner, manager of organizational development and digital transformation at cosmetics and personal care company Weleda, “our values framed what we wanted to do in the digital world, where we set our own limits, where we would go or not go.” The company’s core values are fair treatment, sustainability, integrity, and diversity. So when it came to establishing a robotics process automation program, Weleda executives were careful to ensure that it wasn’t associated with job losses, which would have violated the core value of fair treatment.
2. Extend digital responsibility beyond compliance.
While corporate values provide a useful anchor point for digital responsibility principles, relevant regulations on data privacy, IP rights, and AI cannot be overlooked. Forward-thinking organizations are taking steps to go beyond compliance and improve their behavior in areas such as cybersecurity, data protection, and privacy.
For example, UBS Banking Group’s efforts on data protection were kickstarted by GDPR compliance but have since evolved to focus more broadly on data-management practices, AI ethics, and climate-related financial disclosures. “It’s like puzzle blocks. We started with GDPR and then you just start building upon these blocks and the level moves up constantly,” said Christophe Tummers, head of service line data at the bank.
The key, we have found, is to establish a clear link between digital responsibility and value creation. One way this can be achieved is by complementing compliance efforts with a forward-looking risk-management mindset, especially in areas lacking technical implementation standards or where the law is not yet enforced. For example, Deutsche Telekom (DT) developed its own risk classification system for AI-related projects. The use of AI can expose organizations to risks associated with biased data, unsuitable modeling techniques, or inaccurate decision-making. Understanding the risks and building practices to reduce them are important steps in digital responsibility. DT includes these risks in scorecards used to evaluate technology projects.
Making digital responsibility a shared outcome also helps organizations move beyond compliance. Swiss insurance company Die Mobiliar built an interdisciplinary team consisting of representatives from compliance, business security, data science, and IT architecture. “We structured our efforts around a common vision where business strategy and personal data work together on proactive value creation,” explains Matthias Brändle, product owner of data science and AI.
3. Set up clear governance.
Getting digital responsibility governance right is not easy. Axon had the right idea when it set up an independent AI ethics board. However, the governance was not properly thought through, so when the company disagreed with the board’s recommendation, it fell into a governance grey area marked by competing interests between the board and management.
Setting up a clear governance structure can minimize such tensions. There is an ongoing debate about whether to create a distinct team for digital responsibility or to weave responsibility throughout the organization.
Pharmaceutical company Merck took the first approach, setting up a digital ethics board to provide guidance on complex matters related to data usage, algorithms, and new digital innovations. It decided to act due to an increasing focus on AI-based approaches in drug discovery and big data applications in human resources and cancer research. The board provides recommendations for action, and any decision going against the board’s recommendation needs to be formally justified and documented.
Global insurance company Swiss Re adopted the second approach, based on the belief that digital responsibility should be part of all of the organization’s activities. “Whenever there is a digital angle, the initiative owner who normally resides in the business is responsible. The business initiative owners are supported by experts in central teams, but the business lines are accountable for its implementation,” explained Lutz Wilhelmy, SwissRe risk and regulation advisor.
Another option we’ve seen is a hybrid model, consisting of a small team of internal and external experts, who guide and support managers within the business lines to operationalize digital responsibility. The benefits of this approach includes raised awareness and distributed accountability throughout the organization.
4. Ensure employees understand digital responsibility.
Today’s employees need to not only appreciate the opportunities and risks of working with different types of technology and data, they must also be able to raise the right questions and have constructive discussions with colleagues.
Educating the workforce on digital responsibility was one of the key priorities of the Otto Group, a German e-commerce enterprise. “Lifelong learning is becoming a success factor for each and every individual, but also for the future viability of the company,” explained Petra Scharner-Wolff, member of the executive board for finance, controlling, and human resources. To kickstart its efforts, Otto developed an organization-wide digital education initiative leveraging a central platform that included scores of videos on topics related to digital ethics, responsible data practices, and how to resolve conflicts.
Learning about digital responsibility presents both a short-term challenge of upskilling the workforce, and a longer-term challenge to create a self-directed learning culture that adapts to the evolving nature of technology. As issues related to digital responsibility rarely happen in a vacuum, we recommend embedding aspects of digital responsibility into ongoing ESG skilling programs,that also focus on promoting ethical behavior considering a broader set of stakeholders. This type of contextual learning can help employees navigate the complex facets of digital responsibility in a more applied and meaningful way.
Your organization’s needs and resources will determine whether you choose to upskill your entire workforce or rely on a few specialists. A balance of both can be ideal providing a strong foundation of digital ethics knowledge and understanding across the organization, while also having experts on hand to provide specialized guidance when needed.
Digital responsibility is fast becoming an imperative for today’s organizations. Success is by no means guaranteed. Yet, by taking a proactive approach, forward-looking organizations can build and maintain responsible practices linked to their use of digital technologies. These practices not only improve digital performance, but also enhance organizational objectives.
How to Train Generative AI Using Your Company’s Data
Many companies are experimenting with ChatGPT and other large language or image models. They have generally found them to be astounding in terms of their ability to express complex ideas in articulate language. However, most users realize that these systems are primarily trained on internet-based information and can’t respond to prompts or questions regarding proprietary content or knowledge.
Leveraging a company’s propriety knowledge is critical to its ability to compete and innovate, especially in today’s volatile environment. Organizational Innovation is fueled through effective and agile creation, management, application, recombination, and deployment of knowledge assets and know-how. However, knowledge within organizations is typically generated and captured across various sources and forms, including individual minds, processes, policies, reports, operational transactions, discussion boards, and online chats and meetings. As such, a company’s comprehensive knowledge is often unaccounted for and difficult to organize and deploy where needed in an effective or efficient way.
Emerging technologies in the form of large language and image generative AI models offer new opportunities for knowledge management, thereby enhancing company performance, learning, and innovation capabilities. For example, in a study conducted in a Fortune 500 provider of business process software, a generative AI-based system for customer support led to increased productivity of customer support agents and improved retention, while leading to higher positive feedback on the part of customers. The system also expedited the learning and skill development of novice agents.
Like that company, a growing number of organizations are attempting to leverage the language processing skills and general reasoning abilities of large language models (LLMs) to capture and provide broad internal (or customer) access to their own intellectual capital. They are using it for such purposes as informing their customer-facing employees on company policy and product/service recommendations, solving customer service problems, or capturing employees’ knowledge before they depart the organization.
These objectives were also present during the heyday of the “knowledge management” movement in the 1990s and early 2000s, but most companies found the technology of the time inadequate for the task. Today, however, generative AI is rekindling the possibility of capturing and disseminating important knowledge throughout an organization and beyond its walls. As one manager using generative AI for this purpose put it, “I feel like a jetpack just came into my life.” Despite current advances, some of the same factors that made knowledge management difficult in the past are still present.
The Technology for Generative AI-Based Knowledge Management
The technology to incorporate an organization’s specific domain knowledge into an LLM is evolving rapidly. At the moment there are three primary approaches to incorporating proprietary content into a generative model.
Training an LLM from Scratch
One approach is to create and train one’s own domain-specific model from scratch. That’s not a common approach, since it requires a massive amount of high-quality data to train a large language model, and most companies simply don’t have it. It also requires access to considerable computing power and well-trained data science talent.
One company that has employed this approach is Bloomberg, which recently announced that it had created BloombergGPT for finance-specific content and a natural-language interface with its data terminal. Bloomberg has over 40 years’ worth of financial data, news, and documents, which it combined with a large volume of text from financial filings and internet data. In total, Bloomberg’s data scientists employed 700 tokens, or about 350 billion words, 50 billion parameters, and 1.3 million hours of graphics processing unit time. Few companies have those resources available.
Fine-Tuning an Existing LLM
A second approach is to “fine-tune” train an existing LLM to add specific domain content to a system that is already trained on general knowledge and language-based interaction. This approach involves adjusting some parameters of a base model, and typically requires substantially less data — usually only hundreds or thousands of documents, rather than millions or billions — and less computing time than creating a new model from scratch.
Google, for example, used fine-tune training on its Med-PaLM2 (second version) model for medical knowledge. The research project started with Google’s general PaLM2 LLM and retrained it on carefully curated medical knowledge from a variety of public medical datasets. The model was able to answer 85% of U.S. medical licensing exam questions — almost 20% better than the first version of the system. Despite this rapid progress, when tested on such criteria as scientific factuality, precision, medical consensus, reasoning, bias and harm, and evaluated by human experts from multiple countries, the development team felt that the system still needed substantial improvement before being adopted for clinical practice.
The fine-tuning approach has some constraints, however. Although requiring much less computing power and time than training an LLM, it can still be expensive to train, which was not a problem for Google but would be for many other companies. It requires considerable data science expertise; the scientific paper for the Google project, for example, had 31 co-authors. Some data scientists argue that it is best suited not to adding new content, but rather to adding new content formats and styles (such as chat or writing like William Shakespeare). Additionally, some LLM vendors (for example, OpenAI) do not allow fine-tuning on their latest LLMs, such as GPT-4.
Prompt-tuning an Existing LLM
Perhaps the most common approach to customizing the content of an LLM for non-cloud vendor companies is to tune it through prompts. With this approach, the original model is kept frozen, and is modified through prompts in the context window that contain domain-specific knowledge. After prompt tuning, the model can answer questions related to that knowledge. This approach is the most computationally efficient of the three, and it does not require a vast amount of data to be trained on a new content domain.
Morgan Stanley, for example, used prompt tuning to train OpenAI’s GPT-4 model using a carefully curated set of 100,000 documents with important investing, general business, and investment process knowledge. The goal was to provide the company’s financial advisors with accurate and easily accessible knowledge on key issues they encounter in their roles advising clients. The prompt-trained system is operated in a private cloud that is only accessible to Morgan Stanley employees.
While this is perhaps the easiest of the three approaches for an organization to adopt, it is not without technical challenges. When using unstructured data like text as input to an LLM, the data is likely to be too large with too many important attributes to enter it directly in the context window for the LLM. The alternative is to create vector embeddings — arrays of numeric values produced from the text by another pre-trained machine learning model (Morgan Stanley uses one from OpenAI called Ada). The vector embeddings are a more compact representation of this data which preserves contextual relationships in the text. When a user enters a prompt into the system, a similarity algorithm determines which vectors should be submitted to the GPT-4 model. Although several vendors are offering tools to make this process of prompt tuning easier, it is still complex enough that most companies adopting the approach would need to have substantial data science talent.
However, this approach does not need to be very time-consuming or expensive if the needed content is already present. The investment research company Morningstar, for example, used prompt tuning and vector embeddings for its Mo research tool built on generative AI. It incorporates more than 10,000 pieces of Morningstar research. After only a month or so of work on its system, Morningstar opened Mo usage to their financial advisors and independent investor customers. It even attached Mo to a digital avatar that could speak out its answers. This technical approach is not expensive; in its first month in use, Mo answered 25,000 questions at an average cost of $.002 per question for a total cost of $3,000.
Content Curation and Governance
As with traditional knowledge management in which documents were loaded into discussion databases like Microsoft Sharepoint, with generative AI, content needs to be high-quality before customizing LLMs in any fashion. In some cases, as with the Google Med-PaLM2 system, there are widely available databases of medical knowledge that have already been curated. Otherwise, a company needs to rely on human curation to ensure that knowledge content is accurate, timely, and not duplicated. Morgan Stanley, for example, has a group of 20 or so knowledge managers in the Philippines who are constantly scoring documents along multiple criteria; these determine the suitability for incorporation into the GPT-4 system. Most companies that do not have well-curated content will find it challenging to do so for just this purpose.
Morgan Stanley has also found that it is much easier to maintain high quality knowledge if content authors are aware of how to create effective documents. They are required to take two courses, one on the document management tool, and a second on how to write and tag these documents. This is a component of the company’s approach to content governance approach — a systematic method for capturing and managing important digital content.
At Morningstar, content creators are being taught what type of content works well with the Mo system and what does not. They submit their content into a content management system and it goes directly into the vector database that supplies the OpenAI model.
Quality Assurance and Evaluation
An important aspect of managing generative AI content is ensuring quality. Generative AI is widely known to “hallucinate” on occasion, confidently stating facts that are incorrect or nonexistent. Errors of this type can be problematic for businesses but could be deadly in healthcare applications. The good news is that companies who have tuned their LLMs on domain-specific information have found that hallucinations are less of a problem than out-of-the-box LLMs, at least if there are no extended dialogues or non-business prompts.
Companies adopting these approaches to generative AI knowledge management should develop an evaluation strategy. For example, for BloombergGPT, which is intended for answering financial and investing questions, the system was evaluated on public dataset financial tasks, named entity recognition, sentiment analysis ability, and a set of reasoning and general natural language processing tasks. The Google Med-PaLM2 system, eventually oriented to answering patient and physician medical questions, had a much more extensive evaluation strategy, reflecting the criticality of accuracy and safety in the medical domain.
Life or death isn’t an issue at Morgan Stanley, but producing highly accurate responses to financial and investing questions is important to the firm, its clients, and its regulators. The answers provided by the system were carefully evaluated by human reviewers before it was released to any users. Then it was piloted for several months by 300 financial advisors. As its primary approach to ongoing evaluation, Morgan Stanley has a set of 400 “golden questions” to which the correct answers are known. Every time any change is made to the system, employees test it with the golden questions to see if there has been any “regression,” or less accurate answers.
Legal and Governance Issues
Legal and governance issues associated with LLM deployments are complex and evolving, leading to risk factors involving intellectual property, data privacy and security, bias and ethics, and false/inaccurate output. Currently, the legal status of LLM outputs is still unclear. Since LLMs don’t produce exact replicas of any of the text used to train the model, many legal observers feel that “fair use” provisions of copyright law will apply to them, although this hasn’t been tested in the courts (and not all countries have such provisions in their copyright laws). In any case, it is a good idea for any company making extensive use of generative AI for managing knowledge (or most other purposes for that matter) to have legal representatives involved in the creation and governance process for tuned LLMs. At Morningstar, for example, the company’s attorneys helped create a series of “pre-prompts” that tell the generative AI system what types of questions it should answer and those it should politely avoid.
User prompts into publicly-available LLMs are used to train future versions of the system, so some companies (Samsung, for example) have feared propagation of confidential and private information and banned LLM use by employees. However, most companies’ efforts to tune LLMs with domain-specific content are performed on private instances of the models that are not accessible to public users, so this should not be a problem. In addition, some generative AI systems such as ChatGPT allow users to turn off the collection of chat histories, which can address confidentiality issues even on public systems.
In order to address confidentiality and privacy concerns, some vendors are providing advanced and improved safety and security features for LLMs including erasing user prompts, restricting certain topics, and preventing source code and propriety data inputs into publicly accessible LLMs. Furthermore, vendors of enterprise software systems are incorporating a “Trust Layer” in their products and services. Salesforce, for example, incorporated its Einstein GPT feature into its AI Cloud suite to address the “AI Trust Gap” between companies who desire to quickly deploy LLM capabilities and the aforementioned risks that these systems pose in business environments.
Shaping User Behavior
Ease of use, broad public availability, and useful answers that span various knowledge domains have led to rapid and somewhat unguided and organic adoption of generative AI-based knowledge management by employees. For example, a recent survey indicated that more than a third of surveyed employees used generative AI in their jobs, but 68% of respondents didn’t inform their supervisors that they were using the tool. To realize opportunities and manage potential risks of generative AI applications to knowledge management, companies need to develop a culture of transparency and accountability that would make generative AI-based knowledge management systems successful.
In addition to implementation of policies and guidelines, users need to understand how to safely and effectively incorporate generative AI capabilities into their tasks to enhance performance and productivity. Generative AI capabilities, including awareness of context and history, generating new content by aggregating or combining knowledge from various sources, and data-driven predictions, can provide powerful support for knowledge work. Generative AI-based knowledge management systems can automate information-intensive search processes (legal case research, for example) as well as high-volume and low-complexity cognitive tasks such as answering routine customer emails. This approach increases efficiency of employees, freeing them to put more effort into the complex decision-making and problem-solving aspects of their jobs.
Some specific behaviors that might be desirable to inculcate — either though training or policies — include:
- Knowledge of what types of content are available through the system;
- How to create effective prompts;
- What types of prompts and dialogues are allowed, and which ones are not;
- How to request additional knowledge content to be added to the system;
- How to use the system’s responses in dealing with customers and partners;
- How to create new content in a useful and effective manner.
Both Morgan Stanley and Morningstar trained content creators in particular on how best to create and tag content, and what types of content are well-suited to generative AI usage.
“Everything Is Moving Very Fast”
One of the executives we interviewed said, “I can tell you what things are like today. But everything is moving very fast in this area.” New LLMs and new approaches to tuning their content are announced daily, as are new products from vendors with specific content or task foci. Any company that commits to embedding its own knowledge into a generative AI system should be prepared to revise its approach to the issue frequently over the next several years.
While there are many challenging issues involved in building and using generative AI systems trained on a company’s own knowledge content, we’re confident that the overall benefit to the company is worth the effort to address these challenges. The long-term vision of enabling any employee — and customers as well — to easily access important knowledge within and outside of a company to enhance productivity and innovation is a powerful draw. Generative AI appears to be the technology that is finally making it possible.
11 Ways Tech Adoption Impacts your Small Biz Growth
Small businesses rely heavily on technology to drive development and innovation. Adopting the correct technological solutions can help to streamline processes, increase efficiency, improve client experiences, and create a competitive advantage in the market.
In this post, we will look at how technology contributes to the growth and success of small enterprises.
1. Streamlining Operations
Implementing small business technology solutions can automate and streamline various aspects of small business operations. This includes using project management software, customer relationship management (CRM) systems, inventory management tools, and accounting software. Streamlining operations not only saves time and reduces manual errors but also allows small businesses to allocate resources more efficiently.
Tip: Regularly assess your business processes and identify areas that can be automated or improved with technology. This continuous evaluation ensures that your technology solutions remain aligned with your evolving business needs.
2. Enhancing Customer Engagement
Technology enables small businesses to engage and connect with their customers more effectively. Social media platforms, email marketing software, and customer service tools allow businesses to communicate and build relationships with their target audience. Customer relationship management systems help businesses track customer interactions and preferences, providing insights to deliver personalized experiences and improve customer satisfaction.
Tip: Leverage data from customer interactions to create targeted marketing campaigns and personalized offers. Use automation tools to send timely and relevant messages to your customers, enhancing their engagement and loyalty.
3. Expanding Market Reach
The internet and digital marketing platforms provide small businesses with the opportunity to reach a broader audience beyond their local market. Creating a professional website, utilizing search engine optimization (SEO), and leveraging online advertising channels allow small businesses to attract and engage customers from different regions or even globally. E-commerce platforms enable businesses to sell products or services online, further expanding their market reach.
Tip: Continuously monitor and optimize your online presence to ensure your website is discoverable and user-friendly. Leverage analytics tools to track website traffic, visitor behavior, and conversion rates to make data-driven improvements.
4. Improving Decision-Making with Data
Technology provides small businesses with access to valuable data and analytics, enabling informed decision-making. Through data analysis, businesses can gain insights into customer behavior, market trends, and operational performance. This data-driven approach allows small businesses to make strategic decisions, optimize processes, and identify growth opportunities more effectively.
Tip: Invest in data analytics tools and dashboards that can consolidate and visualize your business data. Regularly review and analyze the data to uncover patterns, identify bottlenecks, and make data-backed decisions to drive growth.
5. Facilitating Remote Work and Collaboration
Advancements in technology have made remote work and collaboration more feasible for small businesses. Cloud-based tools, project management software, and communication platforms enable teams to work together efficiently, regardless of geographical location. This flexibility opens up opportunities to access talent from anywhere, increase productivity, and reduce overhead costs.
Tip: Establish clear communication protocols and project management workflows to ensure effective collaboration among remote teams. Use video conferencing tools for virtual meetings and foster a culture of transparency and accountability to maintain productivity and engagement.
6. Embracing Emerging Technologies
Small businesses should stay informed about emerging technologies that have the potential to transform their industries. Technologies such as artificial intelligence, machine learning, blockchain, and the Internet of Things can offer new opportunities for growth and innovation. Being open to adopting and integrating these technologies into your business strategy can give you a competitive advantage.
7. Data Security and Privacy
Data security and privacy are critical considerations when using technology in small businesses. Implement robust cybersecurity measures, such as firewalls, encryption, and secure data storage, to protect sensitive customer information and intellectual property. Regularly update software and educate employees on best practices for data security to minimize the risk of data breaches.
8. Customer Relationship Management (CRM) Systems
A dedicated CRM system can help small businesses manage customer relationships more efficiently. It allows businesses to track customer interactions, store contact information, and monitor sales pipelines. Utilize CRM software to streamline sales and marketing processes, personalize customer interactions, and nurture long-term customer loyalty.
9. Continuous Learning and Skill Development
Encourage continuous learning and skill development among employees to keep up with technological advancements. Provide access to online courses, training resources, and workshops to enhance digital literacy and proficiency. Embrace a culture of learning and innovation to ensure your small business remains adaptable and competitive in the digital age.
10. Scalable and Flexible Technology Solutions
Choose technology solutions that are scalable and flexible to accommodate your growing business needs. Consider cloud-based software and platforms that allow you to easily scale up or down as your business evolves. This scalability enables small businesses to adapt to changing demands and seize new opportunities without significant disruptions.
11. Regular Technology Assessments
Regularly assess your technology infrastructure to ensure it aligns with your business goals and remains up to date. Conduct technology audits to identify areas for improvement, eliminate outdated systems, and explore new technologies that can drive growth. Stay proactive in evaluating and optimizing your technology stack to maximize its impact on your small business.
Technology serves as a catalyst for small business growth. By leveraging technology effectively and staying agile in an ever-evolving digital landscape, small businesses can unlock their full potential, adapt to changing customer expectations, and drive sustainable growth.
13 Principles for Using AI Responsibly
The competitive nature of AI development poses a dilemma for organizations, as prioritizing speed may lead to neglecting ethical guidelines, bias detection, and safety measures. Known and emerging concerns associated with AI in the workplace include the spread of misinformation, copyright and intellectual property concerns, cybersecurity, data privacy, as well as navigating rapid and ambiguous regulations. To mitigate these risks, we propose thirteen principles for responsible AI at work.
Love it or loath it, the rapid expansion of AI will not slow down anytime soon. But AI blunders can quickly damage a brand’s reputation — just ask Microsoft’s first chatbot, Tay. In the tech race, all leaders fear being left behind if they slow down while others don’t. It’s a high-stakes situation where cooperation seems risky, and defection tempting. This “prisoner’s dilemma” (as it’s called in game theory) poses risks to responsible AI practices. Leaders, prioritizing speed to market, are driving the current AI arms race in which major corporate players are rushing products and potentially short-changing critical considerations like ethical guidelines, bias detection, and safety measures. For instance, major tech corporations are laying off their AI ethics teams precisely at a time when responsible actions are needed most.
It’s also important to recognize that the AI arms race extends beyond the developers of large language models (LLMs) such as OpenAI, Google, and Meta. It encompasses many companies utilizing LLMs to support their own custom applications. In the world of professional services, for example, PwC announced it is deploying AI chatbots for 4,000 of their lawyers, distributed across 100 countries. These AI-powered assistants will “help lawyers with contract analysis, regulatory compliance work, due diligence, and other legal advisory and consulting services.” PwC’s management is also considering expanding these AI chatbots into their tax practice. In total, the consulting giant plans to pour $1 billion into “generative AI” — a powerful new tool capable of delivering game-changing boosts to performance.
In a similar vein, KPMG launched its own AI-powered assistant, dubbed KymChat, which will help employees rapidly find internal experts across the entire organization, wrap them around incoming opportunities, and automatically generate proposals based on the match between project requirements and available talent. Their AI assistant “will better enable cross-team collaboration and help those new to the firm with a more seamless and efficient people-navigation experience.”
Slack is also incorporating generative AI into the development of Slack GPT, an AI assistant designed to help employees work smarter not harder. The platform incorporates a range of AI capabilities, such as conversation summaries and writing assistance, to enhance user productivity.
These examples are just the tip of the iceberg. Soon hundreds of millions of Microsoft 365 users will have access to Business Chat, an agent that joins the user in their work, striving to make sense of their Microsoft 365 data. Employees can prompt the assistant to do everything from developing status report summaries based on meeting transcripts and email communication to identifying flaws in strategy and coming up with solutions.
This rapid deployment of AI agents is why Arvind Krishna, CEO of IBM, recently wrote that, “[p]eople working together with trusted A.I. will have a transformative effect on our economy and society … It’s time we embrace that partnership — and prepare our workforces for everything A.I. has to offer.” Simply put, organizations are experiencing exponential growth in the installation of AI-powered tools and firms that don’t adapt risk getting left behind.
AI Risks at Work
Unfortunately, remaining competitive also introduces significant risk for both employees and employers. For example, a 2022 UNESCO publication on “the effects of AI on the working lives of women” reports that AI in the recruitment process, for example, is excluding women from upward moves. One study the report cites that included 21 experiments consisting of over 60,000 targeted job advertisements found that “setting the user’s gender to ‘Female’ resulted in fewer instances of ads related to high-paying jobs than for users selecting ‘Male’ as their gender.” And even though this AI bias in recruitment and hiring is well-known, it’s not going away anytime soon. As the UNESCO report goes on to say, “A 2021 study showed evidence of job advertisements skewed by gender on Facebook even when the advertisers wanted a gender-balanced audience.” It’s often a matter of biased data which will continue to infect AI tools and threaten key workforce factors such as diversity, equity, and inclusion.
Discriminatory employment practices may be only one of a cocktail of legal risks that generative AI exposes organizations to. For example, OpenAI is facing its first defamation lawsuit as a result of allegations that ChatGPT produced harmful misinformation. Specifically, the system produced a summary of a real court case which included fabricated accusations of embezzlement against a radio host in Georgia. This highlights the negative impact on organizations for creating and sharing AI generated information. It underscores concerns about LLMs fabricating false and libelous content, resulting in reputational damage, loss of credibility, diminished customer trust, and serious legal repercussions.
In addition to concerns related to libel, there are risks associated with copyright and intellectual property infringements. Several high-profile legal cases have emerged where the developers of generative AI tools have been sued for the alleged improper use of licensed content. The presence of copyright and intellectual property infringements, coupled with the legal implications of such violations, poses significant risks for organizations utilizing generative AI products. Organizations can improperly use licensed content through generative AI by unknowingly engaging in activities such as plagiarism, unauthorized adaptations, commercial use without licensing, and misusing Creative Commons or open-source content, exposing themselves to potential legal consequences.
The large-scale deployment of AI also magnifies the risks of cyberattacks. The fear amongst cybersecurity experts is that generative AI could be used to identify and exploit vulnerabilities within business information systems, given the ability of LLMs to automate coding and bug detection, which could be used by malicious actors to break through security barriers. There’s also the fear of employees accidentally sharing sensitive data with third-party AI providers. A notable instance involves Samsung staff unintentionally leaking trade secrets through ChatGPT while using the LLM to review source code. Due to their failure to opt out of data sharing, confidential information was inadvertently provided to OpenAI. And even though Samsung and others are taking steps to restrict the use of third-party AI tools on company-owned devices, there’s still the concern that employees can leak information through the use of such systems on personal devices.
On top of these risks, businesses will soon have to navigate nascent, varied, and somewhat murky regulations. Anyone hiring in New York City, for instance, will have to ensure their AI-powered recruitment and hiring tech doesn’t violate the City’s “automated employment decision tool” law. To comply with the new law, employers will need to take various steps such as conducting third-party bias audits of their hiring tools and publicly disclosing the findings. AI regulation is also scaling up nationally with the Biden-Harris administration’s “Blueprint for an AI Bill of Rights” and internationally with the EU’s AI Act, which will mark a new era of regulation for employers.
This growing nebulous of evolving regulations and pitfalls is why thought leaders such as Gartner are strongly suggesting that businesses “proceed but don’t over pivot” and that they “create a task force reporting to the CIO and CEO” to plan a roadmap for a safe AI transformation that mitigates various legal, reputational, and workforce risks. Leaders dealing with this AI dilemma have important decision to make. On the one hand, there is a pressing competitive pressure to fully embrace AI. However, on the other hand, a growing concern is arising as the implementation of irresponsible AI can result in severe penalties, substantial damage to reputation, and significant operational setbacks. The concern is that in their quest to stay ahead, leaders may unknowingly introduce potential time bombs into their organization, which are poised to cause major problems once AI solutions are deployed and regulations take effect.
For example, the National Eating Disorder Association (NEDA) recently announced it was letting go of its hotline staff and replacing them with their new chatbot, Tessa. However, just days before making the transition, NEDA discovered that their system was promoting harmful advice such as encouraging people with eating disorders to restrict their calories and to lose one to two pounds per week. The World Bank spent $1 billion to develop and deploy an algorithmic system, called Takaful, to distribute financial assistance that Human Rights Watch now says ironically creates inequity. And two lawyers from New York are facing possible disciplinary action after using ChatGPT to draft a court filing that was found to have several references to previous cases that did not exist. These instances highlight the need for well-trained and well-supported employees at the center of this digital transformation. While AI can serve as a valuable assistant, it should not assume the leading position.
Principles for Responsible AI at Work
To help decision-makers avoid negative outcomes while also remaining competitive in the age of AI, we’ve devised several principles for a sustainable AI-powered workforce. The principles are a blend of ethical frameworks from institutions like the National Science Foundation as well as legal requirements related to employee monitoring and data privacy such as the Electronic Communications Privacy Act and the California Privacy Rights Act. The steps for ensuring responsible AI at work include:
- Informed Consent. Obtain voluntary and informed agreement from employees to participate in any AI-powered intervention after the employees are provided with all the relevant information about the initiative. This includes the program’s purpose, procedures, and potential risks and benefits.
- Aligned Interests. The goals, risks, and benefits for both the employer and employee are clearly articulated and aligned.
- Opt In & Easy Exits. Employees must opt into AI-powered programs without feeling forced or coerced, and they can easily withdraw from the program at any time without any negative consequences and without explanation.
- Conversational Transparency. When AI-based conversational agents are used, the agent should formally reveal any persuasive objectives the system aims to achieve through the dialogue with the employee.
- Debiased and Explainable AI. Explicitly outline the steps taken to remove, minimize, and mitigate bias in AI-powered employee interventions—especially for disadvantaged and vulnerable groups—and provide transparent explanations into how AI systems arrive at their decisions and actions.
- AI Training and Development. Provide continuous employee training and development to ensure the safe and responsible use of AI-powered tools.
- Health and Well-Being. Identify types of AI-induced stress, discomfort, or harm and articulate steps to minimize risks (e.g., how will the employer minimize stress caused by constant AI-powered monitoring of employee behavior).
- Data Collection. Identify what data will be collected, if data collection involves any invasive or intrusive procedures (e.g., the use of webcams in work-from-home situations), and what steps will be taken to minimize risk.
- Data. Disclose any intention to share personal data, with whom, and why.
- Privacy and Security. Articulate protocols for maintaining privacy, storing employee data securely, and what steps will be taken in the event of a privacy breach.
- Third Party Disclosure. Disclose all third parties used to provide and maintain AI assets, what the third party’s role is, and how the third party will ensure employee privacy.
- Communication. Inform employees about changes in data collection, data management, or data sharing as well as any changes in AI assets or third-party relationships.
- Laws and Regulations. Express ongoing commitment to comply with all laws and regulations related to employee data and the use of AI.
We encourage leaders to urgently adopt and develop this checklist in their organizations. By applying such principles, leaders can ensure rapid and responsible AI deployment.
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