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Worried about your firm’s AI ethics? These startups are here to help.

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Rumman Chowdhury’s job used to involve a lot of translation. As the “responsible AI” lead at the consulting firm Accenture, she would work with clients struggling to understand their AI models. How did they know if the models were doing what they were supposed to? The confusion often came about partly because the company’s data scientists, lawyers, and executives seemed to be speaking different languages. Her team would act as the go-between so that all parties could get on the same page. It was inefficient, to say the least: auditing a single model could take months.

So in late 2020, Chowdhury left her post to start her own venture. Called Parity AI, it offers clients a set of tools that seek to shrink the process down to a few weeks. It first helps them identify how they want to audit their model—is it for bias or for legal compliance?—and then provides recommendations for tackling the issue.

Parity is among a growing crop of startups promising organizations ways to develop, monitor, and fix their AI models. They offer a range of products and services from bias-mitigation tools to explainability platforms. Initially most of their clients came from heavily regulated industries like finance and health care. But increased research and media attention on issues of bias, privacy, and transparency have shifted the focus of the conversation. New clients are often simply worried about being responsible, while others want to “future proof” themselves in anticipation of regulation.

“So many companies are really facing this for the first time,” Chowdhury says. “Almost all of them are actually asking for some help.”

From risk to impact

When working with new clients, Chowdhury avoids using the term “responsibility.” The word is too squishy and ill-defined; it leaves too much room for miscommunication. She instead begins with more familiar corporate lingo: the idea of risk. Many companies have risk and compliance arms, and established processes for risk mitigation.

AI risk mitigation is no different. A company should start by considering the different things it worries about. These can include legal risk, the possibility of breaking the law; organizational risk, the possibility of losing employees; or reputational risk, the possibility of suffering a PR disaster. From there, it can work backwards to decide how to audit its AI systems. A finance company, operating under the fair lending laws in the US, would want to check its lending models for bias to mitigate legal risk. A telehealth company, whose systems train on sensitive medical data, might perform privacy audits to mitigate reputational risk.

A screenshot of Parity's library of impact assessment questions.
Parity includes a library of suggested questions to help companies evaluate the risk of their AI models.
PARITY

Parity helps to organize this process. The platform first asks a company to build an internal impact assessment—in essence, a set of open-ended survey questions about how its business and AI systems operate. It can choose to write custom questions or select them from Parity’s library, which has more than 1,000 prompts adapted from AI ethics guidelines and relevant legislation from around the world. Once the assessment is built, employees across the company are encouraged to fill it out based on their job function and knowledge. The platform then runs their free-text responses through a natural-language processing model and translates them with an eye toward the company’s key areas of risk. Parity, in other words, serves as the new go-between in getting data scientists and lawyers on the same page.

Next, the platform recommends a corresponding set of risk mitigation actions. These could include creating a dashboard to continuously monitor a model’s accuracy, or implementing new documentation procedures to track how a model was trained and fine-tuned at each stage of its development. It also offers a collection of open-source frameworks and tools that might help, like IBM’s AI Fairness 360 for bias monitoring or Google’s Model Cards for documentation.

Chowdhury hopes that if companies can reduce the time it takes to audit their models, they will become more disciplined about doing it regularly and often. Over time, she hopes, this could also open them to thinking beyond risk mitigation. “My sneaky goal is actually to get more companies thinking about impact and not just risk,” she says. “Risk is the language people understand today, and it’s a very valuable language, but risk is often reactive and responsive. Impact is more proactive, and that’s actually the better way to frame what it is that we should be doing.”

A responsibility ecosystem

While Parity focuses on risk management, another startup, Fiddler, focuses on explainability. CEO Krishna Gade began thinking about the need for more transparency in how AI models make decisions while serving as the engineering manager of Facebook’s News Feed team. After the 2016 presidential election, the company made a big internal push to better understand how its algorithms were ranking content. Gade’s team developed an internal tool that later became the basis of the “Why am I seeing this?” feature.

Gade launched Fiddler shortly after that, in October 2018. It helps data science teams track their models’ evolving performance, and creates high-level reports for business executives based on the results. If a model’s accuracy deteriorates over time, or it shows biased behaviors, Fiddler helps debug why that might be happening. Gade sees monitoring models and improving explainability as the first steps to developing and deploying AI more intentionally.

Arthur, founded in 2019, and Weights & Biases, founded in 2017, are two more companies that offer monitoring platforms. Like Fiddler, Arthur emphasizes explainability and bias mitigation, while Weights & Biases tracks machine-learning experiments to improve research reproducibility. All three companies have observed a gradual shift in companies’ top concerns, from legal compliance or model performance to ethics and responsibility.

“The cynical part of me was worried at the beginning that we would see customers come in and think that they could just check a box by associating their brand with someone else doing responsible AI,” says Liz O’Sullivan, Arthur’s VP of responsible AI, who also serves as the technology director of the Surveillance Technology Oversight Project, an activist organization. But many of Arthur’s clients have sought to think beyond just technical fixes to their governance structures and approaches to inclusive design. “It’s been so exciting to see that they really are invested in doing the right thing,” she says.

O’Sullivan and Chowdhury are also both excited to see more startups like theirs coming online. “There isn’t just one tool or one thing that you need to be doing to do responsible AI,” O’Sullivan says. Chowdury agrees: “It’s going to be an ecosystem.”

Lyron Foster is a Hawaii based African American Musician, Author, Actor, Blogger, Filmmaker, Philanthropist and Multinational Serial Tech Entrepreneur.

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What China’s Big Tech CEOs propose at the annual parliament meeting

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The annual meetings of the Chinese parliament and its advisory body are underway in Beijing this week. Top executives from some of China’s largest tech firms are among the thousands of delegates who attend and put forward their opinions. Here is a look at what the tech bosses are proposing for China’s digital economy.

Pony Ma

More regulatory scrutiny is needed for the country’s budding internet economy, Tencent’s founder and CEO Pony Ma says in one of his proposals, according to a report from the state-backed People’s Posts and Telecommunications News. As a delegate of the National People’s Congress, Ma has submitted over 50 proposals during the parliament meetings over nine consecutive years, said the report.

Specifically, Ma calls for strict governance on peer-to-peer finance, bike-sharing, long-term apartment rental and online grocery group-buying, fledgling areas that have also seen businesses go bust amid cash-hemorrhaging competition.

Ma’s comment comes at a time when regulators are tightening their grips on the country’s tech giants. In recent months, the government has launched probes into Alibaba and other tech firms over anti-competitive practices and proposed a sweeping data law that will limit how platforms collect user information.

Lei Jun

In China’s grand plan to move up the manufacturing value chain, Xiaomi, which makes smartphones and a slew of other hardware devices, has been keen to help factories upgrade.

Xiaomi CEO Lei Jun, a delegate of the NPC, recognizes China is late to smart manufacturing, lacks home-grown innovation and is overreliant on foreign technologies, he says in his proposal. Research and development efforts should be directed to key components such as cutting-edge sensors and precision reducers for factory robots, he says.

China also lacks the talent for advancing factory innovation, Lei points out, thus government policies should support corporations in attracting foreign talent and cultivating collaboration between industries and academia.

Robin Li

As part of its artificial intelligence pivot, Baidu, China’s biggest search engine service, has invested heavily in smart driving tech. Regulation is a major hurdle for autonomous driving firms like Baidu that need large volumes of data to train algorithms, and the rate at which testing permits are issued varies greatly across regions.

Robin Li, CEO of Baidu and a member of the Chinese People’s Political Consultative Conference, urges regulators to be more innovative and pave the way for legal and at-scale commercialization of autonomous driving. A mechanism should be created for various government agencies, industry players and academia to collectively promote the commercial deployment of autonomous driving.

In addition, Li calls for more senior-friendly technologies, greater public access to government data, and better online protection for underage users in China.

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Indonesian logistics startup SiCepat raises $170 million Series B

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SiCepat, an end-to-end logistics startup in Indonesia, announced today it has raised a $170 million Series B funding round. Founded in 2014 to provide last-mile deliveries for small merchants, the company has since expanded to serve large e-commerce platforms, too. Its services now also cover warehousing and fulfillment, middle-mile logistics and online distribution.

Investors in SiCepat’s Series B include Falcon House Partners; Kejora Capital; DEG (the German Development Finance Institution); Telkom Indonesia’s investment arm MDI Ventures; Indies Capital; Temasek Holdings subsidiary Pavilion Capital; Tri Hill; and Daiwa Securities. The company’s last funding announcement was a $50 million Series A in April 2019.

In a press statement, The Kim Hai, founder and chief executive officer of SiCepat’s parent company Onstar Express, said the funding will be used to “further fortify SiCepat’s position as the leading end-to-end logistics service provider in the Indonesian market and potentially to explore expansion to other markets in Southeast Asia.” SiCepat claims to be profitable already and that it was able to fulfill more than 1.4 million packages per day in 2020.

The logistics industry in Indonesia is highly fragmented, which means higher costs for businesses. At the same time, demand for deliveries is increasing thanks to the growth of e-commerce, especially during the COVID-19 pandemic.

SiCepat is one of several Indonesian startups that have raised funding recently to make the supply chain and logistics infrastructure more efficient. For example, earlier this week, supply chain SaaS provider Advotics announced a $2.75 million round. Other notable startups in the space include Kargo, founded by a former Uber Asia executive, and Waresix.

SiCepat focuses in particular on e-commerce and social commerce, or people who sell goods through their social media networks. In statement, Kejora Capital managing partner Sebastian Togelang, said the Indonesian e-commerce market is expected to grow at five-year compounded annual growth rate of 21%, reaching $82 billion by 2025.

“We believe SiCepat is ideally positioned to serve customers from e-commerce giants to uprising social commerce players which contribute an estimated 25% to the total digital commerce economy,” he added.

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InsurGrid raises pre-seed financing to help modernize legacy insurance agents

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Insurance agents spend hours handling paperwork and grabbing client information over the phone. A new seed-stage startup, InsurGrid, has developed a software solution to help ease the process, and make it easier for agents to serve existing clients — and secure new ones.

InsurGrid gives agents a personalized platform to collect information from clients, such as date of birth, driver’s license information and policy declaration. This platform helps agents avoid sitting on long calls or managing back-to-back emails, and instead gives them one spot to understand how all their different clients function. It is starting with property and casualty management.

The startup integrates with 85 insurance carriers, serving as the software layer instead of the provider. Using the InsurGrid platform, insurers can ask clients to upload information and within seconds be registered as a policyholder. This essentially turns into a living Rolodex that insurers can use to access information on the account, and offer quotes on a faster rate.

Image Credits: InsurGrid

There’s a monetary benefit in providing better service. Eden Insurance, a customer of InsurGrid, said that people who submit information through the platform converted at an 82% higher rate than those who don’t. Jeremy Eden, the agency owner of Eden Insurance, said they were able to show consumers that its plan was $300 cheaper than its existing rate.

At the heart of InsurGrid is a bet from the founding team that legacy insurance agents aren’t going anywhere. Co-founder/CEO Chase Beach pointed out that the majority of the $684 billion of annual property and casualty insurance premiums in the United States is distributed by approximately 800,000 agents working in 16,000 brokerages. So far, InsurGrid works with more than 150 of those agencies.

When asked if InsurGrid ever had plans to offer its own insurance, similar to insurtech giants Hippo, Lemonade and Root, Beach said that it is solely working on innovating around the sales process for now. He said that these big companies, which have either recently gone public or are planning to, still rely on agents to be successful.

“Instead of us replacing the insurance agent, what if we gave them that same level of technology of a Hippo or large carrier,” Beach said. “And provide them with the digital experiences so they can compete in 2021.”

As time goes on, he sees insurance agents taking the same role that financial advisors or real estate agents take: “very much involved in the process because they are that expert.”

Other startups that have popped up in this space include Gabi, Trellis and Canopy Connect. The differentiator, the team sees, is that Beach comes from a 144-year-old insurance legacy, giving him key insights on how to sell to agents in a successful and effective way. It is starting with sales, but expect InsurGrid to expand to other parts of the insurance process as well.

To help them compete with new and old startups, InsurGrid recently raised $1.3 million in pre-seed financing to help it fulfill its goal to be the “underdog for the underdogs,” Beach said. Investors include Engineering Capital, Hustle Fund, Vess Capital, Sahil Lavingia and Trevor Kienzle.

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