Today, we use AI with the expectation that it will make us better than we are — faster, more efficient, more competitive, more accurate. Businesses in nearly every industry apply artificial intelligence tools to achieve goals that we would, only a decade or two ago, derided as moonshot dreams. But even as we incorporate AI into our decision-making processes, we can never forget that even as it magnifies our capabilities, so too can it plainly show our flaws.
“Like all technologies before it, artificial intelligence will reflect the values of its creators. So inclusivity matters — from who designs it to who sits on the company boards and which ethical perspectives are included,” AI researcher Kate Crawford once wrote for the New York Times. “Otherwise, we risk constructing machine intelligence that mirrors a narrow and privileged vision of society, with it sold, familiar biases and stereotypes.”
The need for greater inclusivity and ethics-centric research in AI development is well-established — which is why it was so shocking to read about Google’s seemingly senseless firing of AI ethicist Timnit Gebru.
For years, Gebru has been an influential voice in AI research and inclusivity. She cofounded the Black in AI affinity group and speaks as an advocate for diversity in the tech industry. In 2018, she co-wrote an oft-cited investigation into how gender bias influenced Google’s Image Search results. The team Gebru built at Google encompassed several notable researchers and was one of the most diverse working in the AI sector.
“I can’t imagine anybody else who would be safer than me.” Gebru shared in an interview with the Washington Post. “I was super visible. I’m well known in the research community, but also the regulatory space. I have a lot of grass-roots support — and this is what happened.”
So what, exactly, happened?
In November of 2020, Gebru and her team concluded a research paper that examined the potential risks inherent to large language-processing models, which can be used to discern basic meaning from text and, in some cases, create new and convincing copy.
Gebru and her team found three major areas of concern. The first was environmental; relying on large language models could lead to a significant increase of energy consumption and, by extension, our carbon footprint.
The second related to unintended bias; because large language models require massive amounts of data mined from the Internet, “racist, sexist, and otherwise abusive language” could accidentally be included during the training process. Lastly, Gebru’s team pointed out that as large language models become more adept at mimicking language, they could be used to manufacture dangerously convincing misinformation online.
The paper was exhaustively cited and peer-reviewed by over thirty large-language-model experts, bias researchers, critics, and model users. So it came as a shock when Gebru’s team received instructions from HR to either retract the paper or remove the researchers’ names from the submission. Gebru addressed the feedback and asked for an explanation on why retraction was necessary. She received no response other than vague, anonymous feedback and further instructions to retract the paper. Again, Gebru addressed the feedback — but to no avail. She was informed that she had a week to rescind her work.
The back and forth was exhausting for Gebru, who had spent months struggling to improve diversity and advocate for the underrepresented at Google. (Only 1.9 percent of Google’s employee base are Black women.) To be silenced while furthering research on AI ethics and the potential consequences of bias in machine learning felt deeply ironic.
Frustrated, she sent an email detailing her experience to an internal listserv, Google Brain Women and Allies. Shortly thereafter, she was dismissed from Google for “conduct not befitting a Google Manager. Amid the fall out, Google AI head Jeff Dean claimed that the paper “didn’t meet our bar for publication” and “ignored too much relevant research” that undermined the risks she outlined — a shocking accusation, given its breadth of research.
To Gebru, Google’s reaction felt like corporate censorship.
“[Jeff’s email] talks about how our research [paper on large language models] had gaps, it was missing some literature,” she told MIT’s Technology Review. “[The email doesn’t] sound like they’re talking to people who are experts in their area. This is not peer review. This is not reviewer #2 telling you, ‘Hey, there’s this missing citation.’ This is a group of people, who we don’t know, who are high up because they’ve been at Google for a long time, who, for some unknown reason and process that I’ve never seen ever, were given the power to shut down this research.”
“You’re not going to have papers that make the company happy all the time and don’t point out problems,” Gebru concluded in another interview for Wired. “That’s antithetical to what it means to be that kind of researcher.”
We know that diversity and research is crucial to the development of truly effective and unbiased AI technologies. In this context, firing Gebru — a Black, female researcher with extensive accolades for her work in AI ethics — for doing her job is senseless. There can be no other option but to view Google’s actions as corporate censorship.
For context — in 2018, Google developed BERT, a large language model, and used it to improve its search result queries. Last year, the company made headlines by creating large-language techniques that would allow them to train a 1.6-trillion-parameter model four times as quickly as previously possible. Large language models offer a lucrative avenue of exploration for Google; having them questioned by an in-house research team could be embarrassing at best, and limiting at worst.
In an ideal world, Google would have incorporated Gebru’s research findings into its actions and sought ways to mitigate the risks she identified. Instead, they attempted to compel her to revise her document to include cherry-picked “positive” research and downplay her findings. Think about that for a moment — that kind of interference is roughly analogous to a pharmaceutical company asking researchers to fudge the statistics on a new drug’s side effects. Such intervention is not only unethical; it leads to the possibility of real harm.
Then, when that interference failed, Google leadership worked to silence and discredit Gebru. As one writer for Wired concludes, that decision proves that “however sincere a company like Google’s promises may seem — corporate-funded research can never be divorced from the realities of power, and the flows of revenue and capital.”
Gebru is an undeniably strong person, an authority in her field with a robust support network. She had the force of personality to stand her ground against Google. But what if someone who wasn’t quite as well-respected, supported, and brave stood in her position? How much valuable research could be quashed due to corporate politicking? It’s a frightening thought.
The Gebru fallout tells us in no uncertain terms that we need to give real consideration to how much editorial control tech companies should have over research, even if they employ the researchers who produce it. If left unchecked, corporate censorship could stand to usher in the worst iteration of AI: one that writs large our biases, harms the already-underserved, and dismisses fairness in favor of profit.
This article was originally published on BeingHuman.ai
Big data has far and away transcended its status as a technology buzzword. It has become a full-fledged infrastructural norm; countless business leaders have embraced its potential to provide enhanced insight, trend discovery, and other key variables contributing to their annual goals. This notion has created the need for multifaceted implementation strategies which, ideally, aim to use data as a binding agent for all company sectors, ultimately streamlining internal fluidity.
However, despite their ambition and openness to change, many of these leaders fail to recognize that their implementation strategies are flawed — namely in terms of distribution and accessibility. In turn, these inconsistencies can foster a culture of inequality and opacity, creating a counter effect and undermining the very success the strategy strives to achieve.
To curb these setbacks, organizations must be proactive in expanding data knowledge and utilization equally across their different departments.
Diagnosing the problem
To establish a stable data landscape, business leaders need to identify both the internal problem at hand and its broader implications. Limited data distribution should be viewed not only as a threat to corporate functions, but also a potential slight to certain divisions of the organization’s workforce.
The pitfalls of such disparities have already been carefully observed at a societal level, even before the pandemic took them to new heights, and to introduce them to the workplace is to court slow-burning disarray. Inner turmoil can quickly lead to poor external performance, causing companies to fall behind competitors that are more internally cohesive.
With the proper mindset in place, leaders can turn their attention to a variety of strategies to nip their data problem in the bud, and this begins with pinpointing where deficiencies lie. For instance, are employees reliant upon a “suboptimal mix of cloud-based technology and on-premise enterprise systems,” where the collective workforce is hamstrung by patchy, insufficient access — as nearly two-thirds of companies report — or is quality access simply limited to specific parts of the company? Organizations will need to assess workers’ level of “data illiteracy,” a consequence that occurs when data-driven decision-making is limited to select departments and teams.
Leaders must also recognize that the issue can spread beyond data access alone, impacting a company’s confidence in investing and technological innovation. For example, if the company empowers non-IT departments with the bulk of its data technology investment authority, IT workers may start to feel disenfranchised and contribute to a general sense of confusion and mismatched priority. These so-called “shadow systems” are not sustainable because they confuse expectations and leave some workers ill-equipped to address problems that would otherwise be in their wheelhouse.
Creating long-term success
With remote work enduring as a new norm, emphasis on data and technology is arguably at an all-time high, and the need for a tight digital ship has followed suit. Therefore, solutions to the above should be handled with diligence, and it is important to remember that seemingly cut-and-dry remedies are anything but; simply making data access more widespread is not the full answer. Instead, to create lasting success, a broader systemic change should be favored over a temporary band-aid.
By focusing on total reinvention, leaders will be able to properly address each micro-issue contributing to the macro flaw. These focal points could include better, more equal funding to multiple departments, stronger team integration to optimally disperse data knowledge and learning opportunities, and reallocation of investing dollars to reflect future innovation rather than retroactive level setting.
These efforts can also be applied to the introduction (or updating) of relative technology aimed at an improved data-driven work cycle. Access to AI and automation tools, for instance, should be evenly distributed to all applicable company fields — with training provided for those unversed in how to use these resources. Success in each of these areas will be contingent upon properly communicated expectations.
Regardless of where change is most needed, a general rule of thumb is to isolate growth areas that require a rapid return and use them as a kicking-off point. The current system should be audited based on its existing depth and reach, and any salvageable aspects can be leveraged during the construction of a stronger, more efficient successor. New infrastructure must also remain compatible with the business’s technological and financial capabilities.
This type of large-scale change may seem daunting, even unreachable, but it has become an objective necessity as COVID continues to rewrite the rulebook for businesses worldwide. That said, the challenge can be met head-on with a blend of forward-thinking, unfailing commitment, and, above all, constant transparency and attention to detail.
This article was originally published on Business2Community
At this time in 2018, the very idea of bitcoin becoming an accepted currency among major corporations would have been unlikely. Now, the idea still attracts some raised eyebrows — but it isn’t as immediately dismissed.
Cryptocurrency had its first spotlight moment in 2018. It was the modern era’s equivalent of the gold rush; all around the country, adventurous bitcoin dabblers found themselves raking in thousands — sometimes tens of thousands — of dollars after investing comparatively paltry sums.
“Bitcoin and, subsequently, a proliferation of other cryptocurrencies had become an object of global fascination, amid prophecies of societal upheaval and reform, but mainly on the promise of instant wealth,” journalist Nick Paumgarten wrote of the time for The New Yorker. “A peer-to-peer money system that cut out banks and governments had made it possible, and fashionable, to get rich by sticking it to the Man.”
But that promise of high returns soon began to buckle. In January of 2018, the total market capitalization of cryptocurrencies had peaked at $800 billion, skyrocketing up from the mere $18 billion reported the year before. It didn’t take long for the market to plunge; by the end of the year, the market had lost three-quarters of its value and stood at a mere $200 billion.
The cryptocurrency bubble had popped. But unlike other markets, it seemed as though the sheer intensity of the crash would bring the boom-and-bust cycle to a grinding (and permanent) halt. Headlines shared stories of would-be investors who invested their life savings, insurance payouts, and loans in the new market only to see the lion’s share of their hard-earned and borrowed money trickle away.
“What the average Joe hears is how friends lost fortunes,” Alex Kruger, a former banker and current cryptocurrency trader, told reporters for the New York Times. “Irrational exuberance leads to financial overhang and slows progress.”
The response from corporate interests was, at the time, similarly cold. One writer for the Financial Times noted that even well-regarded cryptocurrency enthusiasts were “met with a cold shoulder by US regulators” when they attempted to open exchange-traded funds for Bitcoin and encourage wider adoption.
But in recent months, hints of another crypto boom have begun to circulate. Recent reporting from Forbes’ Ron Shevlin indicates that trading of Bitcoin, Ethereum and other major cryptocurrencies increased sharply at the open of 2020, peaked in February, and remained at high levels through the first half of 2020. Roughly 15 percent of American adults now own cryptocurrency — and notably, half of them invested in the sector for the first time this year.
Corporate America, for its part, hasn’t paid the recovering cryptocurrency market much attention. But, as of October, two major financial firms have diverged from their peers to invest in the opportunity they believe the sector offers. Their names: PayPal and Square.
Early in the month, Square announced that it had purchased a total of 4,709 bitcoins at the cost of roughly $50 million, or one percent of the company’s total assets.
“We believe that bitcoin has the potential to be a more ubiquitous currency in the future,” Square’s Chief Financial Officer, Amrita Ahuja, shared in a press release. “As it grows in adoption, we intend to learn and participate in a disciplined way. For a company that is building products based on a more inclusive future, this investment is a step on that journey.”
PayPal took another route in upholding cryptocurrency. Rather than purchase bitcoin, it launched a cryptocurrency service that will allow customers to buy, hold, and sell digital currency on its site and associated applications. PayPal’s President and CEO, Dan Schulman, explained the company’s decision to create its crypto platform was based on the idea that the “efficiency, speed and resilience of cryptocurrencies” could “give people financial inclusion and access advantages.” Moreover, he said, the eventual shift to such digital currencies was “inevitable.”
But what would this “inevitable” future mean for businesses? If you were to ask Gavin Brown, the co-founder and director at the venture capital firm Blockchain Capital Limited, the answer would be a fundamental change in trade currency.
“In an era where companies such as McDonald’s have a higher credit rating than countries such as Ireland, the notion that multinational firms may issue their own currencies and request that their customers purchase with them is not that outlandish,” CNBC journalist Eustance Heung paraphrased of Brown’s perspective in a 2019 article. “What we’re probably likely to see is … almost like [corporate] groups or alliances coming round around mainstream currencies.”
There are certainly a few benefits to using cryptocurrency in business. Bitcoin and other similar currencies facilitate secure, speedy transactions that offer chargeback protection — because cryptocurrency doesn’t support debt or loans, companies can be sure that payments conveyed via bitcoin aren’t fraudulent or reversible. Bitcoin’s decentralized nature also allows businesses to reach international buyers who may not have previously been able to access their goods or services.
Cryptocurrency offers increased accessibility; however, it isn’t without its detractions.
At present, cryptocurrencies are not stable, insured, or regulated. This lack of clear support from federal bodies makes for tremendous market volatility and puts investors at a high risk of losing their — or their clients’ — fortunes. Most businesses will not want to roll the dice on a currency they can’t rely on.
So, will bitcoin see another, more long-lived, heyday in corporate America? The answer is unclear.
While there might be another cryptocurrency boom on the horizon, it will be a while before bitcoin and its competing currencies come into regular corporate trade. The degree of usage will most likely depend on what we see in the cryptocurrency market over the next few months to a year. Will we see another dramatic boom-bust cycle? Will investors flock to or flee cryptocurrency? Will matters stabilize or devolve once more into wild speculation?
If the market stabilizes and provides more consistent (if less lucrative) returns, we can expect businesses to enter into a period of cautious experimentation. PayPal and Square’s investments have lent bitcoin some credibility. Still, it remains to be seen whether — now that they have been giving tacit industry “permission” — other corporate interests will begin making investments in bitcoin and/or using it in trade.
If cryptocurrency does take off in the corporate sector, it seems likely that federal authorities will begin regulating the market. If we were to reach this point, we would be in a world where cryptocurrency has established an (albeit preliminary) place for itself as a credible form of business currency.
However, even this scenario requires a lot of if’s. It would appear best for companies and institutional investors to approach cryptocurrency conservatively and see how the above hypothetical plays out. Bitcoin may eventually lose its novelty status in big business — but there’s no sense in major corporate players charging forward while its stability remains unclear.
This article was originally published on Medium
The technology holds great promise, no question—but deployment must be done strategically, and with the understanding that you likely won’t see gains on its first attempt to integrate.
If you achieve the improbable often enough, even the impossible stops feeling quite so out of reach.
Over the last several decades, artificial intelligence has permeated almost every American business sector. Its proponents position AI as the tech-savvy executive leader’s magic wand — a tool that can wave away inefficiency and spark new solutions in a pinch. Its apparent success has winched up our suspension of disbelief to ever-loftier heights; now, even if AI tools aren’t a perfect fix to a given challenge, we expect them to provide some significant benefit to our problem-solving efforts.
This false vision of AI’s capability as a one-size-fits-all tool is deeply problematic, but it’s not hard to see where the misunderstanding started. AI tools have accomplished a great deal across a shockingly wide variety of industries.
In pharma, AI helps researchers home in on new drugs solutions; in sustainable agriculture, it can be used to optimize water and waste management; and in marketing, AI chatbots have revolutionized the norms of customer service interactions and made it easier than ever for customers to find straightforward answers to their questions quickly.
Market research provides similar backing to AI’s versatility and value. In 2018, PwC released a report which noted that the value derived from the impact of AI on consumer behavior (i.e., through product personalization or greater efficiency) could top $9.1 trillion by 2030.
McKinsey researchers similarly note that 63 percent of executives whose companies have adopted AI say that the change has “provided an uptick in revenue in the business areas where it is used,” with respondents from high performers nearly three times likelier than those from other companies to report revenue gains of more than 10 percent. Forty-four percent say that the use of AI has reduced costs.
Findings like these paint a vision of AI as having an almost universal, plug-and-play ability to improve business outcomes. We’ve become so used to AI being a “fix” that our tendency to be strategic about how we deploy such tools has waned.
Earlier this year, a joint study conducted by the Boston Consulting Group and MIT Sloan Management Review found that only 11 percent of the firms that have deployed artificial intelligence sees a “sizable” return on their investments.
This is alarming, given the sheer volume that investors are putting into AI. Take the healthcare industry as an example; in 2019, surveyed healthcare executives estimated that their organizations would invest an average of $39.7 million over the following five years. To not receive a substantial return on that money would be disappointing, to say the very least.
As reported by Wired, the MIT/BCG report “is one of the first to explore whether companies are benefiting from AI. Its sobering finding offers a dose of realism amid recent AI hype. The report also offers some clues as to why some companies are profiting from AI and others appear to be pouring money down the drain.”
What, then, is the main culprit? According to researchers, it seems to be a lack of strategic direction during the implementation process.
“The people that are really getting value are stepping back and letting the machine tell them what they can do differently,” Sam Ransbotham, a professor at Boston College who co-authored the report, commented. “The gist is not blindly applying AI.”
The study’s researchers found that the most successful companies used their early experiences with AI tools — good or ill — to improve their business practices and better-orient artificial intelligence within their operations. Of those who took this approach, 73 percent said that they saw returns on their investments. Companies who paired their learning mindset with efforts to improve their algorithms also tended to see better returns than those who took a plug-and-play approach.
“The idea that either humans or machines are going to be superior, that’s the same sort of fallacious thinking,” Ransbotham told reporters.
Scientific American writers Griffin McCutcheon, John Malloy, Caitlyn Hall, and Nivedita Mahesh put Ransbotham’s point another way in an article titled — tellingly — “AI Isn’t the Solution to All of Our Problems.” They write:
“The belief that AI is a cure-all tool that will magically deliver solutions if only you can collect enough data is misleading and ultimately dangerous as it prevents other effective solutions from being implemented earlier or even explored. Instead, we need to both build AI responsibly and understand where it can be reasonably applied.”
In other words: We need to stop viewing AI as a fix-it tool and more as a consultant to collaborate with over months or years. While there’s little doubt that artificial intelligence can help business leaders cultivate profit and improve their business, their deployment of the technology must be done strategically — and within the understanding that the business probably won’t see the gains it hopes for on its first attempt to integrate AI.
If business leaders genuinely intend to make the most of the opportunity that artificial intelligence presents, they should be prepared to workshop. Adopt a flexible, experimental, and strategic mindset. Be ready to adjust your business operations to address any inefficiencies or opportunities the technology may spotlight — and, by that same token, take the initiative to continually hone your algorithms for greater accuracy. AI can provide guidance and inspiration, but it won’t offer outright answers.
Businesses are investing millions — often tens of millions — in AI technology. Why not take the time to learn how to use it properly?
This article was originally published on ChiefExecutive.net
Of all the circumstances that we might have imagined kickstarting America’s smart city aspirations, a pandemic surely wasn’t on our list. And yet, our anxieties over disease transmission might just be the fuel that propels us towards a future in which autonomous cars become the urban norm.
A huge setback for public transit
For the last several months, the COVID-19 pandemic has compelled us to change our perspectives to suit a newly disease-aware world. We’ve adapted our day-to-day routine to suit social distancing recommendations and become leery of crowded, high-traffic areas. Our faith in public transit, in particular, has been shaken so profoundly that it very nearly demands an innovative fix. Time magazine recently described COVID-19’s impact on public transit as “apocalyptic.”
“[Buses] that once carried anywhere from about 50 to 100 passengers have been limited to between 12 and 18 to prevent overcrowding in response to coronavirus […] Seattle transit riders have described budgeting as much as an extra hour per trip to account for the reduced capacity, eating into their time at work, school or with family,” Time’s Alejandro de la Garza wrote in July.
Sometimes, riders’ anxieties compel them to leave the bus before their stop; one woman who de la Garza interviewed described exiting several stops early with her seven-year-old son after the driver allowed a crowd of people to board at once.
“It’s very trying,” the source, Brittany Williams, shared. “I’ll put it in those terms.”
How can we keep public transit viable?
The obvious answer to the overcrowding and slow-transit problems would be to add more buses — but such a move doesn’t seem economically feasible with the current decline in public transit use. In July, the Transit App reported a 58 percent year-over-year reduction in travelers within Williams’ home city of Seattle.
Numbers are a little worse in Washington D.C., with a 66 percent decline in Metrobus use and a 90 percent drop in Metrorail traffic. The losses experienced in New York City are among the worst, with the Transit App noting a 95 percent loss in the spring and a still-alarming reduction rate of 84 percent in late summer.
Pandemic fears have limited traveling, which in turn has limited fares to a trickle and all but eliminated cities’ abilities to add to their public transit fleets. According to a recent McKinsey report, 52 percent of American respondents travel less than they did before COVID-19. Many who do travel opt for a private vehicle over bus or train trips. A full third of surveyed consumers say that they “value constant access to a private vehicle more than before COVID-19.”
To risk stating the obvious: not everyone can buy or store a public car, nor should they even if they could. The environmental impact of replacing public transit with individual vehicles would be environmentally disastrous and dramatically exacerbate existing traffic and parking problems. Moreover, reports indicate that purchasing intent has dropped with the economic downturn; people don’t want to buy new cars when their incomes are uncertain.
An opening for autonomous cars
But I would argue that city-dwellers don’t necessarily need private cars — they just need a mode of transport that offers the isolated, sterilized feel of personal vehicles with the cost-efficiency and dependability that characterizes good public transit. Ridesharing services like Uber and Lyft have set the groundwork for this, but aren’t a perfect fit. They’re expensive, focused on one person at a time, and naturally pose a virus-spread risk to passengers and drivers alike. But what if there were no drivers, only a limited number of masked and isolated passengers traveling pre-defined, regular routes?
Years ago, architect Peter Calthorpe painted a vision of California cities with autonomous cars that was very nearly this, writing: “Down the center of El Camino, on dedicated, tree-lined lanes, [would be] autonomous shuttle vans. They’d arrive every few minutes, pass each other at will, and rarely stop, because an app would group passengers by destination.”
There’s a window of opportunity to reshape consumer perception of autonomous cars within a public-transit perception. Instead of anxiously fleeing buses inundated with close-seated crowds, mothers like Brittany Williams could order an autonomous ride and sit, as per a COVID-optimized version of Calthorpe’s vision, either alone or with one or two distanced others. Between routes, these cars could be sanitized and sent off to support new passengers. Such an approach would establish self-driving vehicles not as a one-person luxury, but a new and COVID-thoughtful form of public transportation.
The sustainability and convenience benefits of adding a self-driving shuttle service to public transit are countless. These include lessening the need for private cars, mitigating traffic deadlock, and improving passenger convenience. Autonomous shuttles could shoulder at least some of the burden carried by other public transit services and lessen the need for additional (if half-filled) buses and trains.
While it is true that Uber and Lyft have been talking about developing autonomous cars and next-gen taxi services for years to no avail, we are now closer than ever before to achieving viable autonomous driving technology. Earlier this year, the GM-backed driverless car startup Cruise received a permit from the California DMV that would allow the company to test driverless cars without safety drivers, albeit only on specific roads.
This represents a significant step forward in the deployment of autonomous cars and, if successful, could lead to the first fully-autonomous vehicles. It is worth noting that despite delays, Cruise hopes to launch a self-driving taxi service soon; its fourth-generation autonomous cars features automatic doors, rear-seat airbags, and, notably, no steering wheel.
If Cruise can manage to accomplish this, it stands to reason that autonomous shuttles are not all that far away. If anything, cities might have more opportunities to partner with self-driving startups and incorporate autonomous shuttles into municipal transit. Given that pandemic-prompted anxieties will likely persist until (if not well beyond) the emergence of a mass-produced vaccine, it seems likely that the window of opportunity for piquing consumer interest in socially-distanced autonomous transit could extend out over years.
Of course, there are few clear speed bumps in the way.
For one, there is still a pervasive stigma around the perceived safety of autonomous cars. Uber memorably halted its experiments in 2018, when one of its experimental vehicles struck and killed pedestrian Elaine Herzberg in Tempe, Arizona.
At the time, there were rumors that the company planned to divest itself of its self-driving interests entirely; however, the company has begun to restart its efforts on a significantly smaller scale in recent months. Cruise — and any other autonomous car startup that takes on the challenge — will need to assure the public of its products’ safety before it can achieve widespread acceptance.
Another major issue will be cost.
With public transit in such dire straits, obtaining the funds for a partnership between self-driving car startups and municipal transit may prove difficult in the short term unless the local government is convinced of the public’s need for autonomous shuttles and the revenue that such an approach could attract as a result of said need. Proponents will need to launch a media campaign to raise public awareness and bolster backing for adding autonomous shuttles to municipal transit.
If we can get beyond some of these initial hurdles, we can kickstart a smart, sustainable and COVID-aware urban transit system. As with the early days of online shopping, consumer perceptions of autonomous driving will quickly shift from it being a laughable luxury to a must-have public service, especially under pandemic conditions.
Originally published on TriplePundit.com
Once more, titans of industry have fallen under censure for perceived monopolization and the abuse of their considerable power. But this time, their names aren’t Carnegie, Rockefeller, or Vanderbilt, but Bezos, Zuckerberg, Pichai, and Cook.
In recent weeks, all four have faced hard questions about perceived corporate misbehavior. The concerns directed towards each corporate icon may differ according to the specifics of their company’s actions, but all ask the same essential question: Can massive tech companies keep themselves from intimidating or using the small businesses that increasingly rely on their platforms to survive?
In late July, the House Judiciary Committee convened a hearing to address the matter. The event marked the culmination of an extensive antitrust investigation that encompassed over a million corporate documents and hundreds of hours of personnel interviews. One reporter for the Verge characterized the hearing as “one of the biggest tech oversight moments in recent years.” Representative David Cicilline, the Commercial and Administrative Law Subcommittee Chair, made the subcommittee’s belief in the importance of the hearing clear at its outset.
“Because these companies are so central to our modern life, their business practices and decisions have an outsized effect on our economy and our democracy,” Cicilline said. “Any single action by any one of these companies can affect hundreds of millions of us in profound and lasting ways.”
Cicilline further argued that each of the four tech companies under investigation — Amazon, Facebook, Google, and Apple — comprised a crucial channel for distribution, such as an app store or ad venue, and uses monopolizing methods to purchase or otherwise block potential competitors. He also noted that the companies all either show preference to their branded products or create pricing schemes that undermine third-party brands’ abilities to compete.
As you might have already guessed, each case has a wealth of associated information and considerations. Recapping them, let alone providing commentary, would be challenging at best. So, instead, I want to consider the question of whether or not a business can be both a market ecosystem and fair competitor through the context of one business: Amazon.
Amazon fell under fire earlier this year, when the Wall Street Journal released a stunning report that the e-retailer had used data from its third-party sellers — data that was believed to be proprietary — to inform the development and sale of competing, private-label products.
This revelation sent shockwaves through the business community, despite the fact that it wasn’t entirely unanticipated; according to reporting from the Verge, the European Union’s main antitrust body claimed that it was “investigating whether Amazon is abusing its dual role as a seller of its own products and a marketplace operator and whether the company is gaining a competitive advantage from data it gathers on third-party sellers” in 2019.
Amazon has pushed back on these concerns, claiming that it has policies that forbid private-label personnel from obtaining specific seller data. However, the Wall Street Journal’s interviews of former and current employees found that the rule was inconsistently enforced and overlooked so often that the use of third-party, proprietary data was openly discussed in product development meetings.
“We knew we shouldn’t,” one former employee said while recounting a pattern of using seller data to launch and bolster Amazon products. “But at the same time, we are making Amazon branded products, and we want them to sell.”
And therein lies the core of the problem. Amazon is a company that maintains a laser focus on success — even to the point that its employees are willing to circumvent policy for its sake. But we can’t blame the employees, not entirely. The tech industry has long been known for its move-fast-and-break-things attitude, and Amazon more than most; the e-retailer’s obsession with achievement is near-legendary.
In 2015, New York Times reporters Jodi Kantor and David Streitfeld published an exposé that painted Amazon’s culture as one specifically designed for intense, high-output, and unforgiving efficiency.
“Every aspect of the Amazon system amplifies the others to motivate and discipline the company’s marketers, engineers and finance specialists: the leadership principles; rigorous, continuing feedback on performance; and the competition among peers who fear missing a potential problem or improvement and race to answer an email before anyone else,” Kantor and Streitfeld described.
“The culture stoked their willingness to erode work-life boundaries, castigate themselves for shortcomings (being ‘vocally self-critical’ is included in the description of the leadership principles) and try to impress a company that can often feel like an insatiable taskmaster.”
The article even noted that Amazon holds yearly firing sessions (dubbed “cullings” in the exposé) to shed those who don’t perform up to its notoriously high standards. Illness, parenthood, and even family loss — none were considered excuses for lapses in performance.
Given the stressful environment and achievement-at-all-costs mentality, is it any surprise that employees would sneak around a barely-enforced policy to obtain data that will help their projects succeed? I would say no.
In a culture that positions cutthroat competitiveness as a professional survival mechanism, an anticompetitive policy is little more than flimsy caution tape: readily seen, easily circumvented, and meant more to provide plausible deniability than to prevent anyone from breaking the rules.
And, of course, we have to acknowledge the point that a company that periodically culls its staff for the sake of efficiency wouldn’t mind pushing blame onto a worker who happens to get caught. Bezos already did so in his hearing. He testified, “What I can tell you is we have a policy against using seller-specific data to aid our private label business but I can’t guarantee that policy has never been violated.”
Another hearing exchange between Cicilline and Bezos is particularly telling.
Cicilline asks, “Isn’t it an inherent conflict of interest for Amazon to produce and sell products that compete directly with third party sellers, particularly when you, Amazon, set the rules of the game?”
Bezos responds: “The consumer is the one making the decisions.”
But how is that an appropriate response, when the data Amazon collects allows the e-retailer an unfair advantage to design and market products designed to outstrip the competition? It remains to be seen whether legislators will ultimately choose to spin off Amazon marketplace from its Basics line, but Amazon has proven beyond a doubt that it is naive to believe that a company that was built with a crush-the-competition mentality should be trusted with safeguarding smaller, vulnerable competitors’ proprietary data.
Company culture beats policy, every time.
Originally published on Medium
Bennat Berger is an NYC-based tech writer, investor, and entrepreneur. He is the founder of Novel Property Ventures, a company that specializes in finding, acquiring, and managing high-potential multifamily residential units in New York City. Berger is also the founder of Novel Private Equity, a private equity firm that gives tech startups the support they need to thrive in an increasingly competitive business market.