“Some Zen teachers say that the way the body takes care of us, without our even being aware of it, is an example of the beautiful and continuous functioning of our Original Nature, the inherent goodness and wisdom of our being. Our hand pulls back from fire before we even register heat, our eyes blink before we are aware of a sharp sound, our hand reaches out to catch something before we know it is falling.”

Before I was diagnosed with esophageal cancer a year and a half ago, I rather jauntily told the readers of my memoirs that when faced with extinction I wanted to be fully conscious and awake, in order to “do” death in the active and not the passive sense. And I do, still, try to nurture that little flame of curiosity and defiance: willing to play out the string to the end and wishing to be spared nothing that properly belongs to a life span. However, one thing that grave illness does is to make you examine familiar principles and seemingly reliable sayings. And there’s one that I find I am not saying with quite the same conviction as I once used to: In particular, I have slightly stopped issuing the announcement that “Whatever doesn’t kill me makes me stronger.” In fact, I now sometimes wonder why I ever thought it profound. It is usually attributed to Friedrich Nietzsche: Was mich nicht umbringt macht mich stärker. In German it reads and sounds more like poetry, which is why it seems probable to me that Nietzsche borrowed it from Goethe, who was writing a century earlier. But does the rhyme suggest a reason? Perhaps it does, or can, in matters of the emotions. I can remember thinking, of testing moments involving love and hate, that I had, so to speak, come out of them ahead, with some strength accrued from the experience that I couldn’t have acquired any other way. And then once or twice, walking away from a car wreck or a close encounter with mayhem while doing foreign reporting, I experienced a rather fatuous feeling of having been toughened by the encounter. But really, that’s to say no more than “There but for the grace of god go I,” which in turn is to say no more than “The grace of god has happily embraced me and skipped that unfortunate other man.”

shorter version of that piece, also highlights some of the research underway to address the challenges of bias in AI and suggests six pragmatic ways forward. Will AI’s decisions be less biased than human ones? Or will AI make these problems worse? Two opportunities present themselves in the debate. The first is the opportunity to use AI to identify and reduce the effect of human biases. The second is the opportunity to improve AI systems themselves, from how they leverage data to how they are developed, deployed, and used, to prevent them from perpetuating human and societal biases or creating bias and related challenges of their own. Realizing these opportunities will require collaboration across disciplines to further develop and implement technical improvements, operational practices, and ethical standards.

Self-Selection Bias In: Encyclopedia of Survey Research Methods Encyclopedia Edited by: Paul J. Lavrakas Published: 2008 DOI: https://dx.doi.org/10.4135/9781412963947.n526 + More information Online Publication Date: January 1, 2011 Disciplines: Anthropology, Business and Management, Communication and Media Studies, Criminology and Criminal Justice, Economics, Education, Geography, Health, Marketing, Nursing, Political Science and International Relations, Psychology, Social Policy and Public Policy, Social Work, Sociology Print ISBN: 9781412918084 | Online ISBN: 9781412963947 Buy in print Entry Reader’s Guide Entries A-Z Subject Index Search form Download PDF Show page numbers Self-selection bias is the problem that very often results when survey respondents are allowed to decide entirely for themselves whether or not they want to participate in a survey. To the extent that respondents' propensity for participating in the study is correlated with the substantive topic the researchers are trying to study, there will be self-selection bias in the resulting data. In most instances, self-selection will lead to biased data, as the respondents who choose to participate will not well represent the entire target population.

Develop plans to cover classes in the event of increased staff absences. Coordinate with other local child care programs and reach out to substitutes to determine their anticipated availability if regular staff members need to stay home if they or their family members are sick.

I once was threatened by the father of the bride and had the [police] haul him off all because his daughter didn't pay for a cake, so I didn't make it. We do everything online, through a payment plan. You do your down payment after we agree on design/flavours from the tasting, and then sign a contract. In that contract it states that if the cake isn't paid for in advance, I don't make it. We send you reminder emails and such, so it's not like we just straight up ignore you until it's go time, but still, it's your responsibility to pay. His daughter never made payments on her cake so my payment system cancelled it and refunded her money minus the downpayment, as is standard.