Telling lies and faking emotions is quite common in human-human interactions: though there are risks, in many situations such behaviours provide social benefits. In recent years, there have been many social robots and chatbots that fake emotions or behave deceptively with their users. In this paper, I present a few examples of such robots and chatbots, and analyze their ethical aspects. Three scenarios are presented where some kind of lying or deceptive behaviour might be justified. Then five approaches to deceptive behaviours - no deception, blatant deception, tactful deception, nudging, and self deception - are discussed and their implications are analyzed. I conclude by arguing that we need to develop localized and culture-specific solutions to incorporating deception in social robots and chatbots.

It is utopian to rule out any form of anthropomorphism when addressing a conversational assistant because of the use of language as a vector of exchange. Designers, therefore, must limit these shortcomings with the implementation of these design rules, thus reducing the risks of deception and dependency, and giving confidence in these systems.

Artificial Intelligence is, without a doubt, one of the Fourth Industrial Revolution's primary growth engines. The benefits and business potential inherent in this technology are immense. Improving customer experience, automating business processes, real-time information analysis, improving cyber protection capabilities, and implementing autonomous applications are just a few examples of these benefits. However, and similarly to other types of new and groundbreaking technologies, we must consider the latent risks in implementing Artificial Intelligence in an uncontrolled manner. Considering such risks is evidently even more urgent as Artificial Intelligence has now become so vastly used that it affects every aspect of our personal and professional life and used in scale, in large sectors of the economy.

Identifying what someone is feeling or even anticipating potential reactions based on nonverbal behavioral cues is no longer a problem reserved for sensitive and astute people. With the advancement of cutting-edge technologies in emotional intelligence, this capability gains new dimensions with the capability of machines recognizing human emotions for a variety of purposes. Complex facial detection algorithms are now powerful enough to analyze and measure emotions captured in real-world situations. They are so powerful that we are reaching a point that some ethical aspects have been raised. Emotion Recognition is based on facial expression recognition, a computer-based technology that employs algorithms to detect faces, code facial expressions, and recognize emotional states in real-time.

Artificial intelligence (AI) is here, and its benefits are seemingly limitless. There is a flip side though--there always is. AI experts, and those involved with AI, are concerned that if we do not proceed with caution, some of the strange things predicted in science-fiction movies such as 2001: A Space Odyssey may be more truth than fiction. Elon Musk told The New York Times that his experience with AI at Tesla allows him to say with confidence, "We're headed toward a situation where AI is vastly smarter than humans." He adds, "That doesn't mean everything goes to hell in five years. It just means that things get unstable or weird."

An interdisciplinary team at the Technical University of Munich (TUM) advocates the integration of ethics from the very beginning of the development process of new technologies. Alena Buyx, professor of ethics in medicine and health technologies, explains the embedded ethics approach. The discussions surrounding a greater emphasis on ethics in AI research have greatly intensified in recent years, to the point that one might speak of "ethics hype," and many committees in Germany and around the world such as the German Ethics Council or the EU Commission High-Level Expert Group on Artificial Intelligence have responded. They are all in agreement: More ethics are required in the development of AI-based health technologies. But how do things look in practice for engineers and designers?

As the second installment in this series of posts, I will touch upon on the topic of privacy in data science and algorithms. In particular, I'm going to discuss a relatively novel concept of privacy called differential privacy that promises, similar to algorithmic fairness, a way of quantifying the privacy of AI algorithms. When we, as humans, talk about privacy, we mostly refer to a desire to not be observed by others. However, what does privacy mean in the context of algorithms that "observe" us by using data that has information on us? In a very general sense, we could say that privacy will be preserved if, after analysis, the algorithm that used our data (e.g. an application on our smartphones) doesn't know anything about us.

The discriminatory biases of the algorithms, the invasion of privacy, the risks of facial recognition and the regulation of human-machine relations are challenges that AI needs to face. However, the interests of governments and large companies often prevail over good practices. Artificial intelligence (AI) is no longer a science fiction thing, it is everywhere. Your bank uses it to know if it is going to give you a credit or not and the ads you see on your social networks come out of a classification carried out by an algorithm, which has microsegmented and'decided' if it shows you offers of wrinkle creams or high-end cars. Facial recognition systems, which use airports and security forces, are also based on this technology.

Ethics is one of those topics that everyone has a slightly different definition or view of what it means. The Oxford english dictionary defines ethics as, 'Moral principles that govern a person's behaviour or the conducting of an activity'. As you can imagine this topic can be difficult to discuss and has many, many different aspects. In the era of AI, Machine Learning, Data Science, etc the topic of Ethics is finally becoming an important topic. Again there are many perspective on this.

At the beginning of the year, KPMG published a list with the "top 5 AI hires companies need to succeed in 2019", with the "AI ethicist" ranking among them. Such lists definitely strengthen my'raison d'être'. Yet, when I tweeted something on this, reactions showed that not everyone shares this opinion. There is obviously no common understanding whether we need AI ethicists in the first place, and whether creating such a profile inevitably leads to "machinewashing". Let me address these concerns below and argue what it takes to really make AI ethicists a "top hire".