This study investigates how personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) and demographic factors (age, education, and income) are associated with perceptions of artificial intelligence (AI) outcomes exhibiting gender and racial bias and with general attitudes towards AI. Analyses of a large-scale experiment dataset (N = 1,206) indicate that digital self-efficacy and technical knowledge are positively associated with attitudes toward AI, while liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism. Furthermore, age and income are closely connected to cognitive gaps in understanding discriminatory AI outcomes. These findings highlight the importance of promoting digital literacy skills and enhancing digital self-efficacy to maintain trust in AI and beliefs in AI usefulness and safety. The findings also suggest that the disparities in understanding problematic AI outcomes may be aligned with economic inequalities and generational gaps in society. Overall, this study sheds light on the socio-technological system in which complex interactions occur between social hierarchies, divisions, and machines that reflect and exacerbate the disparities.

I was recently a guest speaker at the Sales Leadership Conference organized by Dr. Karen Peesker, Co-Founder of the Sales Leadership Institute, a department at the Toronto Metropolitan University (formally Ryerson University) in Toronto, Canada. The conference was hosted by the Ted Rogers School of Management at Toronto Metropolitan University (Formerly Ryerson University) in collaboration with HEC Montreal and Ivey, funded by SSHRC, IT World Canada, Microsoft, DHL, Rogers, RBC, CPSA, and other community leaders. The conference goals were to bring university professors, students, industry leaders, and academicians to share their learning programs, identify gaps and requirements to advance the sales profession and most importantly, tackle a vision for the future of sales. The strongest theme of the conference was the business imperative for advancing digital literacy, data literacy and ensuring that technology was firmly embedded in all sales learning programs. Digital literacy is best defined as an individual's ability to find, evaluate, and clearly communicate information and knowledge through using diverse digital platforms.

The adoption of artificial intelligence in recent years has seen amplified momentum. From enhancing human capabilities to automating repetitive tasks and streamlining customer services to improving business efficiency, AI is already making its way into everyday business processes. According to PwC research, AI is likely to add US$15.7 trillion to global economic growth by 2030. While the technology's acceptance in mainstream society is becoming a new phenomenon, it has been around decades ago. The recent advances in AI significantly have augmented human cognition and decisions, taking every aspect of people's lives and business by storm. The rapid deployment of AI across industries is majorly driven by the growing investment in technology.

A few months ago, in January 2018, the European Parliament's Legal Affairs Committee approved a report that outlines a possible legal framework to regulate the interactions between a) humans, and b) robots and Artificial Intelligence systems. The report is quite revolutionary. It proposes, e.g., giving certain types of robots and AI systems personhood, as "electronic persons": These electronic persons would have rights and obligations, and the report suggests that they should obey Isaac Asimov's Laws of Robotics. The report also advises that the manufacturers of robots and AI systems should build in a'kill switch' to be able to deactivate them. Another recommendation is that a European Agency for Robotics and AI be established that would be capable of responding to new opportunities and challenges arising from technological advancements in robotics. The EU is not alone in its desire to regulate AI: similar (though less far reaching) reports were published in Japan and in the UK.

A few months ago, in January 2018, the European Parliament's Legal Affairs Committee approved a report that outlines a possible legal framework to regulate the interactions between a) humans, and b) robots and Artificial Intelligence systems. The report is quite revolutionary. It proposes, e.g., giving certain types of robots and AI systems personhood, as "electronic persons": These electronic persons would have rights and obligations, and the report suggests that they should obey Isaac Asimov's Laws of Robotics. The report also advises that the manufacturers of robots and AI systems should build in a'kill switch' to be able to deactivate them. Another recommendation is that a European Agency for Robotics and AI be established that would be capable of responding to new opportunities and challenges arising from technological advancements in robotics. The EU is not alone in its desire to regulate AI: similar (though less far reaching) reports were published in Japan and in the UK.

As incredible new technologies enter the market, they bring new opportunities for both businesses and individuals. The challenge is balancing these with ethics. One of my favourite quotes is from Arthur C Clarke: "Any sufficiently advanced technology is indistinguishable from magic". At World Tour Sydney recently, we heard many inspirational stories of the implementation of AI and other Fourth Industrial Revolution (4IR) technologies, and I couldn't help but think that people living a generation ago would describe them as some kind of sorcery. More importantly, each was unique and original, shining a light on the new reality of business done well in a world where everything is connected and intelligent.

What if an insurance company had a tool that improved profitability by predicting insurance rates and premiums with greater precision and efficiency? Or if a natural gas utility company improved safety, efficiency, and customer satisfaction with a tool to predict and better respond to leaks or unaccounted-for gas? These are both examples of machine learning techniques that are gaining momentum due to the pervasiveness of data (from the Internet of Things [IoT], social media and mobile devices) and the seemingly infinite scalability of cloud-based compute power. At the same time, the understanding and sophistication of algorithms have expanded, and the ability to apply complex mathematical calculations to data, and rapidly process them, is also driving growing interest in exploiting machine learning to gain competitive advantages in business. Machine learning is a type of data analysis technology that extracts knowledge without being explicitly programmed to do so.