A new BCG survey of large organizations found that almost half of those that believe they have a mature implementation of a Inteligência artificial responsável (RAI) o programa está, na realidade, atrasado. Até as organizações que relataram lançar a IA em escala superestimaram seu progresso da RAI: menos da metade tem um programa RAI totalmente maduro. Essa descoberta é particularmente importante porque uma organização não pode alcançar a IA verdadeira em escala sem garantir que esteja desenvolvendo sistemas de IA com responsabilidade.
Os quatro estágios da maturidade da RAI
To assess organizations’ progress in implementing RAI programs—the structures, processes, and tools that help organizations ensure their AI systems work in the service of good while transforming their businesses—we collected and analyzed data from senior executives at more than 1,000 large organizations. (See the sidebar “Our Survey Methodology.”) We then categorized these organizations into four distinct stages of RAI maturity: lagging (14%), developing (34%), advanced (31%), and leading (21%). An organization’s stage reflects its progress in reaching maturity across seven generally accepted dimensions of RAI. These dimensions include fairness and equity, data and privacy governance, and human plus AI. The latter one is to ensure that AI systems are designed to empower people, preserve their authority over AI systems, and safeguard their well-being.
The organizations that are in the leading stage have reached maturity across all the dimensions. These organizations have defined RAI principles as well as achieved enterprise-wide adoption of RAI policies and processes. These organizations are clearly making the most of their relationship with AI.
As organizations progress from lagging to leading, each stage is marked by substantial accomplishments, particularly in the areas of fairness and equity as well as human plus AI. This finding is important because organizations’ RAI programs don’t tend to initially focus on these dimensions, and they are the most difficult to address. Accomplishments in these areas are therefore highly indicative of broader maturation in RAI, and they signal that an organization is ready to transition to the next stage of maturity. Meanwhile, organizations consistently focus first on the area of data and privacy governance. This is a logical result, given that regulations and policies often mandate this focus.
When looking across industries and regions, in turn, we found that an organization’s region is a better predictor of its maturity than its industry: Europe and North America, respectively, have the highest average RAI maturity. In contrast, we found few significant differences in maturity across industries, although a higher concentration of RAI leaders can be found in the technology, media, and telecommunications industry and in industrial goods.
Organizations’ Perceptions Often Do Not Match Reality
The survey reveals that many organizations overestimate their RAI progress. We asked the executives how they would define their organization’s progress on its RAI journey, whether it had made no progress (2% of respondents), had defined RAI principles (11%), had partially implemented RAI (52%), or had fully implemented RAI (35%). We then compared each executive’s response with our assessment of the organization’s maturity. Our evaluation was based on respondents’ answers to 21 questions about their implementation across the seven dimensions.
Os resultados são surpreendentes. Descobrimos que cerca de 55% de todas as organizações - de Laggers a líderes - são menos avançadas do que eles acreditam. É importante ressaltar que mais da metade (54%) daqueles que acreditam que implementaram totalmente os programas RAI superestimaram seu progresso. Este grupo, em particular, é preocupante. Como eles acreditam ter implementados em programas RAI totalmente implementados, é provável que eles façam mais investimentos, embora as lacunas permaneçam claramente. Das organizações que relataram que desenvolveram e implementaram IA em escala, menos da metade tem recursos de RAI em pé de igualdade com essa implantação.
We also found that many organizations with advanced AI capabilities are behind in implementing RAI programs. Of the organizations that reported they have developed and implemented AI at scale, less than half have RAI capabilities on a par with that deployment. Alcançando a IA em escala Não apenas requer a criação de recursos robustos técnicos e de habilitação de humanos, mas também implementando totalmente um programa RAI. Para essas organizações, ficar aquém da maturidade total em todas as dimensões da RAI significa que elas ainda não alcançaram seu nível percebido de implantação de IA em escala.
RAI Is Much More Than Risk Mitigation
Although C-suite executives and boards of directors are concerned with the organizational risks posed by a lapse of an AI system, Argumentamos Que as empresas não devem buscar RAI simplesmente para mitigar o risco. Em vez disso, as organizações devem ver o RAI como uma oportunidade para fortalecer o relacionamento com as partes interessadas e obter benefícios comerciais significativos.
Parece que a maioria das organizações concorda. Quando solicitado a selecionar o principal motivo para buscar a RAI, mais de 40% escolheram seus potenciais benefícios comerciais - mais do que o dobro da porcentagem que selecionou a mitigação de riscos. Além disso, descobrimos que, à medida que a maturidade da RAI das organizações cresce, sua motivação para capturar benefícios comerciais através da RAI. Simultaneamente, o foco na mitigação de risco diminui. Maturidade
Best Practices for Reaching RAI Maturity
Os líderes RAI têm consistentemente políticas e processos que são totalmente implantados em suas organizações que cobrem todas as sete dimensões da RAI. Nessas organizações líderes, encontramos vários marcadores -chave que são indicativos de maturidade mais ampla de RAI. As ações rastreadas e mitigadoras são implantadas proativamente, caso sejam detectadas. Minimizada. Mesmo para aqueles que o fazem, é importante aprofundar seus esforços para procurar mais oportunidades de melhorar. Slideshow. Imagem>
- Both the individuals responsible for AI systems and the business processes that use these systems adhere to their organization’s principles of RAI.
- The requirements and documentation of AI systems’ design and development are managed according to industry best practices.
- Biases in historical data are systematically tracked, and mitigating actions are proactively deployed in case issues are detected.
- Security vulnerabilities in AI systems are evaluated and monitored in a rigorous manner.
- The privacy of users and other people is systematically preserved in accordance with data use agreements.
- The environmental impact of AI systems is regularly assessed and minimized.
- All AI systems are designed to foster collaboration between humans and machines while minimizing the risk of adverse impact.
Organizations that do not follow these practices or do not have them fully deployed are most likely not leading in RAI and should dig more deeply into their RAI efforts. Even for those that do, digging deeper into their efforts to look for further opportunities to improve is important.
For more detail on our survey results, see the following slideshow.
