Summary: This is the third in our series on chatbots. In this installment we'll look at the best practice dos and don'ts as described by a number of successful chatbot developers. In our first article we covered the chatbot basics including their brief technological history, uses, basic design choices, and where deep learning comes into play. The second article focused on the universal NLU front ends for all chatbots and some of the technical definitions and programming particulars necessary to understand how these really function. In this article, we've scoured the internet for advice from successful chatbot developers to provide some useful best practices, or at least some valuable dos and don'ts. The user doesn't care that you've got a chatbot.

Key Points: – Businesses often overlook important issues related to morals and ethics of chatbots and AI – Customers need to know when they are communicating with a machine and not an actual human – Ownership of information shared with a bot is another key ethical consideration and can create intellectual property issues – The privacy and protection of user data is paramount in today's interconnected world You can also listen to The Modern Customer Podcast with Rob High here.) Businesses are rapidly waking up to the need for chatbots and other self-service technology. From automating basic communications and customer service, to reducing call center costs and providing a platform for conversational commerce, chatots offer many new opportunities to delight and better serve consumers. Chatbots can offer 24/7 customer service, rapidly engaging users, answering their queries as whenever they arrive. Millennials in particular are impatient when engaging with brands and expect real-time responses.

In September, Michal Kosinski published a study that he feared might end his career. The Economist broke the news first, giving it a self-consciously anodyne title: "Advances in A.I. Are Used to Spot Signs of Sexuality." But the headlines quickly grew more alarmed. By the next day, the Human Rights Campaign and Glaad, formerly known as the Gay and Lesbian Alliance Against Defamation, had labeled Kosinski's work "dangerous" and "junk science." In the next week, the tech-news site The Verge had run an article that, while carefully reported, was nonetheless topped with a scorching headline: "The Invention of A.I. 'Gaydar' Could Be the Start of Something Much Worse."

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.

Welcome to California Inc., the weekly newsletter of the L.A. Times Business Section. Good times in the Golden State: We learned Friday that California added 31,700 net jobs in October and the state unemployment rate fell to 4.9% from 5.1% a month earlier. The latest state jobs report follows a strong September, when employers boosted payrolls by a revised 50,300. In October, the leisure and hospitality sector, and the educational and health services sector, saw the largest gains. The Automobile Club of Southern California says this year's Thanksgiving holiday will be the busiest locally since 2007, with 3.87 million residents expected to get away for the long weekend.

MIT Technology Review has a meandering article, "A.I Can Be Made Legally Responsible for It's Decisions". In it's own way, it tries to chart the territories of trade secrets and corporations, threading a needle that we may actually need to change to adapt to using Artificial Intelligence (AI). One of the things that surprises me in such writing and conversations is not that it revolves around protecting trade secrets – I'm sorry, if you put your self-changing code out there and are willing to take the risk, I see that as part of it – is that it focuses on the decision process. Almost all bad decisions in code I have encountered have come about because the developers were hidden in a silo behind a process that isolated them… sort of like what happens with an AI, only two-fold. If the decision process is flawed, the first thing to be looked at is the source data for the decisions – and in an AI, this can be a daunting task as it builds learning algorithms based on… data.

The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before---applications range from clinical decision support to autonomous driving and predictive policing. That said, there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems. There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation, and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. In this work, we review contexts in which explanation is currently required under the law, and then list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans.

Australia's Chief Scientist, Alan Finkel, has called on governments and businesses across the world to consider developing a regulatory framework for artificial intelligence devices, ranging from the likes of Apple's Siri to weaponised drones. Dr Finkel, who was speaking at the Creative Innovation Global conference, said he was optimistic about AI, but an ethical stamp needed to be developed, similar to a Fair Trade label, in order to give consumers trust that the AI in a device had been developed according to specified global standards. "Two years ago I published an article in Cosmos magazine calling for a global accord [on weaponised drones]. In the same year, more than 3000 AI and robotics researchers signed an open letter urging the leaders of the world to take action to prevent a global arms race," he said. "On the other end of the spectrum are tools in everyday use, such as social media platforms and smartphones.

Artificial Intelligence (AI) has been used extensively in automatic decision making in a broad variety of scenarios, ranging from credit ratings for loans to recommendations of movies. Traditional design guidelines for AI models focus essentially on accuracy maximization, but recent work has shown that economically irrational and socially unacceptable scenarios of discrimination and unfairness are likely to arise unless these issues are explicitly addressed. This undesirable behavior has several possible sources, such as biased datasets used for training that may not be detected in black-box models. After pointing out connections between such bias of AI and the problem of induction, we focus on Popper's contributions after Hume's, which offer a logical theory of preferences. An AI model can be preferred over others on purely rational grounds after one or more attempts at refutation based on accuracy and fairness. Inspired by such epistemological principles, this paper proposes a structured approach to mitigate discrimination and unfairness caused by bias in AI systems. In the proposed computational framework, models are selected and enhanced after attempts at refutation. To illustrate our discussion, we focus on hiring decision scenarios where an AI system filters in which job applicants should go to the interview phase.

A United Nations panel agreed Friday to consider guidelines and potential limitations for military uses of artificial intelligence amid concerns from human rights groups and other leaders that so-called "killer robots" could pose a long-term, lethal threat to humanity. Advocacy groups warned about the threats posed by such "killer robots" and aired a chilling video illustrating their possible uses on the sidelines of the first formal U.N. meeting of government experts on Lethal Autonomous Weapons Systems this week. More than 80 countries took part. Ambassador Amandeep Gill of India, who chaired the gathering, said participants plan to meet again in 2018. He said ideas discussed this week included the creation of legally binding instrument, a code of conduct, or a technology review process.