AI-Based Safety-Critical Systems (AI-SCS) are being increasingly deployed in the real world. These can pose a risk of harm to people and the environment. Reducing that risk is an overarching priority during development and operation. As more AI-SCS become autonomous, a layer of risk management via human intervention has been removed. Following an accident it will be important to identify causal contributions and the different responsible actors behind those to learn from mistakes and prevent similar future events. Many authors have commented on the "responsibility gap" where it is difficult for developers and manufacturers to be held responsible for harmful behaviour of an AI-SCS. This is due to the complex development cycle for AI, uncertainty in AI performance, and dynamic operating environment. A human operator can become a "liability sink" absorbing blame for the consequences of AI-SCS outputs they weren't responsible for creating, and may not have understanding of. This cross-disciplinary paper considers different senses of responsibility (role, moral, legal and causal), and how they apply in the context of AI-SCS safety. We use a core concept (Actor(A) is responsible for Occurrence(O)) to create role responsibility models, producing a practical method to capture responsibility relationships and provide clarity on the previously identified responsibility issues. Our paper demonstrates the approach with two examples: a retrospective analysis of the Tempe Arizona fatal collision involving an autonomous vehicle, and a safety focused predictive role-responsibility analysis for an AI-based diabetes co-morbidity predictor. In both examples our primary focus is on safety, aiming to reduce unfair or disproportionate blame being placed on operators or developers. We present a discussion and avenues for future research.

Raquel Urtasun, the former chief scientist at Uber ATG, is the founder and CEO of Waabi, an autonomous vehicle startup that came out of stealth mode last week. The Toronto-based company, which will focus on trucking, raised an impressive $83.5 million in a Series A round led by Khosla Ventures. Urtasun joined Mobility 2021 to talk about her new venture, the challenges facing the self-driving vehicle industry and how her approach to AI can be used to advance the commercialization of AVs. Urtasun, who is considered a pioneer in AI, led the R&D efforts as a chief scientist at Uber ATG, which was acquired by Aurora in December. Six months later, we have Waabi.

After years of development and some shocking incidents, the next step for Uber's self-driving unit could be a sale. As first reported by TechCrunch and later Reuters, anonymous sources say Uber is in talks with self-driving tech startup Aurora about a sale, and the discussions have gone on since October. Last year Toyota, Softbank and Denso teamed up to invest $1 billion in Uber ATG in a deal that valued the self-driving focused spinoff at $7.25 billion. Meanwhile the smaller Aurora has had investments from Amazon, and deals with Hyundai but also lost one with Volkswagen last year, while counting big names like ex-Google Chris Urmson among its ranks. The risk and uncertainty in developing self-driving tech has been evident in the history of Uber ATG, which includes the death of a pedestrian and its former self-driving lead receiving an 18 month sentence for stealing tech from his former employer, Waymo.

Eighteen months ago, Uber's self-driving car unit, Uber Advanced Technologies Group, was valued at $7.25 billion following a $1 billion investment from Toyota, DENSO and SoftBank's Vision Fund. Now, it's up for sale and a competing autonomous vehicle technology startup is in talks with Uber to buy it, according to three sources familiar with the deal. Aurora Innovation, the startup founded by three veterans of the autonomous vehicle industry who led programs at Google, Tesla and Uber, is in negotiations to buy Uber ATG. Terms of the deal are still unknown, but sources say the two companies have been in talks since October and it is far along in the process. An Uber spokesperson declined to comment, citing that the company's general policy is not to comment on these sorts of inquiries.

Deep learning (DL) is advancing very quickly and different DL frameworks are effective at different tasks. As a result, we've used several DL frameworks at Uber ATG over the last few years. In 2016, Caffe2 was our primary deep learning framework, and in early 2017 we put in a significant amount of work to integrate TensorFlow. This involved major integration hurdles with CUDA and cuDNN, conflicts between dependencies of Caffe2 and TensorFlow, library loading issues and more. In late 2017, we started developing more models in PyTorch.

For the first time, ride-hailing company Uber has opened up about what is going on under the hood of their ATG's machine learning infrastructure and versioning control platform for autonomous driving vehicles. ATG is the Advanced Technologies Group, which concentrates and researches on self-driving vehicles by deploying machine learning models into the cars. The self-driving division at Uber has more than 450 employees who have been working on autonomous vehicle technology for several years now. Recently, the self-driving team at Uber developed a set of tools and microservices to support the ML workflow known as VerCD. The team also discussed their self-driving vehicle components, which use machine learning models as well as the machine learning model life cycle.

As Uber finally closes in on its IPO, its self-driving car unit is getting a big cash infusion and some independence. The company announced tonight that Toyota, Denso and Softbank are investing a total of $1 billion in its Advanced Technologies Group (Uber ATG), in a deal that values that part of the company at $7.25 billion. This adds onto Toyota's $500 million investment last year, which the two said would lead to the creation of an autonomous fleet based on Toyota's Sienna minivan. So far, many of the big car companies are teaming up to develop autonomous tech combined with ridesharing angles as it's expected to be a huge market in the next few years. According to Uber CEO Dara Khosrowshahi, "The development of automated driving technology will transform transportation as we know it, making our streets safer and our cities more livable. Today's announcement, along with our ongoing OEM and supplier relationships, will help maintain Uber's position at the forefront of that transformation."

My focus at CMU was developing trust in human-robot interactions. I researched how a robot can effectively convey its intention to humans and how humans can foster more trusting relationships with machines that they regularly interact with to accomplish simple tasks. At CMU, I also served as a research intern in a lab that was developing new modules for the Braille Writing Tutor (a device that uses audio feedback to teach individuals to write braille) and researched ways to extend the service to devices such as smart phones. When I worked on the Braille Writing Tutor, I learned the value of feedback, receiving input on how to create a user-centric design from the students and teachers using it on a daily basis. Developing a technology that people actually used and hearing from them directly that it improved their lives was incredibly satisfying.