Interaction between humans and AI systems raises the question of how people understand AI systems. This has been addressed with explainable AI, the interpretability arising from users' domain expertise, or collaborating with AI in a stable environment. In the absence of these elements, we discuss designing Actionable AI, which allows non-experts to configure black-box agents. In this paper, we experiment with an AI-powered cartpole game and observe 22 pairs of participants to configure it via direct manipulation. Our findings suggest that, in uncertain conditions, non-experts were able to achieve good levels of performance. By influencing the behaviour of the agent, they exhibited an operational understanding of it, which proved sufficient to reach their goals. Based on this, we derive implications for designing Actionable AI systems. In conclusion, we propose Actionable AI as a way to open access to AI-based agents, giving end users the agency to influence such agents towards their own goals.

Artificial intelligence and human resources collaborate to help save money, enhance planning, and, most significantly, transform companies. The collaboration between artificial intelligence and human resources increases employee performance and expertise and lowers costs. AI technology in HR aids organizations in gaining a complete understanding of their staff's behaviors and inclinations. This data may be used to improve employee happiness by enhancing the job experience. AI is also used to assist human resources professionals in various areas of their profession, from early applicant shortlisting through performance evaluation.

I hope that you enjoy the latest AI news and insights, don't forget to comment with your feedback. From this week you can find some interesting stuff added to the last section. But they have had a hard time shaking infighting and controversy over a variety of issues. Biased datasets are often the source for why AI models are also biased. "Adoption and scaling aren't things you add at the tail end of a project; they're where you need to start," Join 6000 aspiring Data Scientists to watch this FREE 75-minute session.

As the people charged with designing, building, and deploying artificial intelligence -- from data engineers to developers -- recognize, AI is a powerful mechanism for amplifying human knowledge, skills, and efficiency. But how can AI proponents employ AI to fix a moribund or toxic corporate culture? Entrepreneurs and experts at the front lines of the AI revolution recognize this is a hurdle technology alone can't solve, "AI cannot solve issues where there are already underlying problems, like a company's culture or lack of trust from a customer base," says Stephan Baldwin, founder of Assisted Living Center. "These are fostered by principles that shape the everyday inner and outer workings of a company." One of the challenges, Baldwin points out, us "artificial Intelligence models act based on historical data, meaning they're prone to biases that we humans had when gathering information. Sometimes, an automated process doesn't take into account the people it governs."

With all the hype over artificial intelligence (AI) and machine learning (ML) these days, understanding how your company can really make use of them can be a bewildering experience. Hype cycles are always tough, but for AI and ML, they can be particularly alienating. Press and analyst coverage tends to focus on the technical minutiae, making it difficult to see the relevance to your organization or to see your organization as an eligible candidate to adopt and apply AI. While looking at AI from the perspective of a data scientist is no doubt interesting, for many organizations, doing so is simply getting too far into the weeds. Yes, you can learn about frameworks, algorithms and hyperparameters, but that's a bit like studying combustion engines in pursuit of learning how to drive.