Blog posts by Anne Bailey
Regulation has the uncomfortable task of limiting untapped potential. I was surprised when I recently received the advice to think of life like a box. “The walls of this box are all the rules you should follow. But inside the box, you have perfect freedom.” Stunned as I was at the irony of having complete freedom to think inside the box, those at the forefront of AI development and implementation are faced with the irony of limiting projects with undefined potential.
Although Artificial General Intelligence – the ability of a machine to intuitively react to situations that it has not been trained to handle in an intelligent, human way – is still unrealized, narrow AI that enables applications to independently complete a specified task is becoming a more accepted addition to a business’ digital toolkit. Regulations that address AI are built on preexisting principles, primarily data privacy and protection against discrimination. They deal with the known risks that come with AI development. In 2018, biometric data was added to the European GDPR framework to require extra protection. In both the US and Europe, proposals are currently being discussed to monitor AI systems for algorithmic bias and govern facial recognition use by public and private actors. Before implementing any AI tool, companies should be familiar with the national laws for the region in which they operate.
These regulations have a limited scope, and in order to address the future unknown risks that AI development will pose, a handful of policy groups have published guidelines that attempt to set a model for responsible AI development.
The major bodies of work include:
- The Montreal Declaration for Responsible Development of AI from the University of Montreal and Fonds de Recherche du Quebec (published December 2018)
- Guidelines on Artificial Intelligence and Data Protection from the Council of Europe (published January 2019)
- Ethics Guidelines on Trustworthy AI from The EU Commission (published April 2019)
- The OECD Principles on AI from the OECD (published May 2019)
The principles developed by each body are largely similar. The main principles that all guidelines discussed address the need for developers and AI implementers to protect human autonomy, obey the rule of law, prevent harm and promote inclusive growth, maintain fairness, develop robust, prudent, and secure technology, and ensure transparency.
The single outstanding feature is that only one document provides measurable and immediately implementable action. The EU Commission included an assessment for developers and corporate AI implementors to conduct to ensure that AI applications become and remain trustworthy. The assessment is currently in a pilot phase and will be updated in January 2020 to reflect the comments from businesses and developers. The other guidelines offer compatible principles but are general enough to allow any of the public, private, or individual stakeholders interacting with AI to deflect responsibility.
This collection of guidelines from the international community are not legally binding restrictions, but are porous barriers that allow sufficiently cautious and responsible innovations to grow and expand as the trustworthiness of AI increases. The challenge in creating regulations for an intensely innovative industry is to build in flexibility and the ability to mitigate unknown risks without compromising the artistic license. These guidelines attempt to set an ethical example to follow, but it is essential to use tools like the EU Commission’s assessment tool which establish an appropriate responsibility, no matter the status as developer, implementor, or user.
Alongside the caution from governing bodies comes a clear that AI development can bring significant economic, social, and environmental growth. The US issued an executive order in February 2019 to prioritize AI R&D projects, while the EU takes a more cautiously optimistic approach by building of recognizing the opportunities but prioritizing building and maintaining a uniform EU strategy for AI adoption.
If you liked this text, feel free to browse our Artificial Intelligence focus area for more related content.
Thanks to an incessant desire to remove repetitive tasks from our to-do lists, researchers and companies are developing AI solutions to HR – namely to streamline recruiting, improve the employee experience, and to assess performance.
AI driven HR management will look different in small businesses than in large companies and multinationals. There are different barriers that will have to be navigated, but also different priorities and opportunities that small businesses will have with AI.
Smaller budgets create price barriers to implementing an AI system, and likely psychological barriers as the self-built CEO resists delegating tasks that would otherwise rely on his or her gut instinct. Access to a sufficient quantity of data to optimize algorithms is perhaps the largest challenge that small businesses will face when integrating AI into their HR practices. Companies typically gather data from their own databases, assembling a wide range of hiring documents, employee evaluations, etcetera. Large companies have decades of stored HR data from thousands of employees, and clearly have an advantage when it comes to gathering a large volume of usable data.
In terms of priorities, there is a huge divide between the value proposition that AI offers to large and small businesses. Big companies need to leverage time-saving aspects, especially to create a customized connection for thousands of employees. Routine communication, building employee engagement, and monitoring employee attrition are all aspects that minimize repetitive work and save time. In a sense, the goal is to give institutional bureaucracy a personal touch – like a small business has. A small company’s strengths come from its unique organizational culture, which is heavily dependent on natural, human interaction and well-designed teams. It is this “small company” feel that large companies try to imitate with AI customization features.
Of course, small companies also need to save time, especially because many do not have a dedicated HR department – in some cases, the department consists of one person dividing time between their main role and HR tasks. Their time is limited, so instead of implementing FAQ chatbots that make the organization feel small and accessible, small businesses should focus on another area which consumes too much time: recruiting and promoting visibility.
Finding qualified and competitive candidates is challenging when a firm’s circle of influence is geographically limited. A factor often contributing to success in small firms is the ability to hire for organizational fit, thus building tightly knit teams to deliver agile service. To increase the chances of attracting highly qualified candidates, small businesses should focus on using AI systems to support recruiting and hiring for organizational fit.
Small businesses are always under pressure to do more with less. When implementation costs are high and internal resources limited, small businesses can consider plug and play tools which rely on external datasets. For those who are open to experiment, they can look for AI projects that have overlap with their goals. For example, socially minded companies looking to attract more diverse applicants can participate in studies like AI-enabled refugee resettlement, placing people in areas where they will be most likely to find employment. A project like this could shift setup costs for implementing new technology and achieve wider HR goals that the company may have, like gaining employees with specific skills that are not common in the area, opening up more opportunities for innovation through diversity, gaining different language capabilities, and so on.
The risk of using AI technologies to support hiring has already played out in the case of Amazon. With the best intentions, the research team designing a hiring tool to select the highest qualified candidates based on their resumes noticed that their algorithm had learned to value traits that indicated the candidate was male, and penalize indicators that the candidate was female. The cause was imbedded in their input data: the CVs and associated data given to the system to learn from was influenced by years of gendered hiring practices. The project was quietly put to rest. This example was luckily only a pilot version and wasn’t the deciding factor in any applications, but provides a valuable lesson to developers and adopters of recruitment AI: maintaining transparency throughout development and beyond will illuminate weaknesses with time. Robust checks by outside parties will be necessary, because one’s own biases are most difficult to see.
AI can have a role to play in small business HR strategies just as much as the large corporations. But as with any strategy, the decision should be aimed at delivering clear advantages with a plan to mitigate any risks.
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AI for the Future of your Business: Effective, Safe, Secure & Ethical Everything we admire, love, need to survive, and that brings us further in creating a better future with a human face is and will be a result of intelligence. Synthesizing and amplifying our human intelligence have therefore the potential of leading us into a new era of prosperity like we have not seen before, if we succeed keeping AI Safe, Secure and Ethical. Since the very beginning of industrialization, and even before, we have been striving at structuring our work in a way that it becomes accessible for [...]